├── .gitattributes ├── .gitignore ├── data ├── country_gdp.pkl ├── country_papers.pkl ├── country_gdp__202102.pkl ├── world_population.pkl ├── country_papers__202102.pkl └── neurips_conf_data.pkl ├── LICENSE ├── README.md └── scripts ├── extract_population.py ├── extract_citations.py ├── extract_countries.py ├── neurips_download.py └── neurips_download.ipynb /.gitattributes: -------------------------------------------------------------------------------- 1 | *.pkl filter=lfs diff=lfs merge=lfs -text 2 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | .ipynb_checkpoints/ 4 | plots 5 | -------------------------------------------------------------------------------- /data/country_gdp.pkl: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:2631d8d5f2f388834623118b1eb56e377d24e8c2365bbf02dafdc00530610825 3 | size 705 4 | -------------------------------------------------------------------------------- /data/country_papers.pkl: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:83fdf8f1d43843ef3fb28088019843763767e214cfa884752b595383ffa974ed 3 | size 481 4 | -------------------------------------------------------------------------------- /data/country_gdp__202102.pkl: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:2631d8d5f2f388834623118b1eb56e377d24e8c2365bbf02dafdc00530610825 3 | size 705 4 | -------------------------------------------------------------------------------- /data/world_population.pkl: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:5462644c86a53a9c025258c9df5a1dc1480286023e8f4add2353df45a99c400f 3 | size 1950 4 | -------------------------------------------------------------------------------- /data/country_papers__202102.pkl: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:83fdf8f1d43843ef3fb28088019843763767e214cfa884752b595383ffa974ed 3 | size 481 4 | -------------------------------------------------------------------------------- /data/neurips_conf_data.pkl: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:a4c593efc01a3c0a1859e99f94bd46ff10af408cc8cfe4d138d9359589c8d475 3 | size 428707084 4 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 nemanja-rakicevic 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Conference historical data analysis 2 | 3 | This repository contains `scripts` to obtain the conference data, 4 | as well as additional metadata. 5 | 6 | The `data` directory contains alredy extracted data. 7 | 8 | 9 | 10 | ## NeurIPS conference 11 | 12 | All the plots and analysis featured in the Towards Data Science [blogpost](https://towardsdatascience.com/neurips-conference-historical-data-analysis-e45f7641d232) were generated with 13 | the [notebook](https://github.com/nemanja-rakicevic/conference_historical_data_analysis/blob/main/analysing_data__neurips.ipynb). 14 | 15 | 16 | ## Citation 17 | 18 | > Rakicevic, Nemanja. (Feb 2021). NeurIPS Conference: Historical Data Analysis. Towards Data Science blog. https://towardsdatascience.com/neurips-conference-historical-data-analysis-e45f7641d232. 19 | 20 | Or 21 | 22 | ``` 23 | @article{rakicevic2021neuripsanalysis, 24 | title = "NeurIPS Conference: Historical Data Analysis.", 25 | author = "Rakicevic, Nemanja", 26 | journal = "towardsdatascience.com", 27 | year = "2021", 28 | month = "Feb", 29 | url = "https://towardsdatascience.com/neurips-conference-historical-data-analysis-e45f7641d232" 30 | } 31 | ``` 32 | -------------------------------------------------------------------------------- /scripts/extract_population.py: -------------------------------------------------------------------------------- 1 | 2 | """ 3 | Description: Script for extracting the total world population by year. 4 | 5 | """ 6 | 7 | import os 8 | import pickle 9 | import requests 10 | from bs4 import BeautifulSoup 11 | 12 | 13 | search = "https://www.worldometers.info/world-population/world-population-by-year/" 14 | html_text = requests.get(search).text 15 | soup = BeautifulSoup(html_text, 'html.parser') 16 | 17 | world_population = {} 18 | for ss in soup.find_all("tr"): 19 | if hasattr(ss.contents[1], "text") and (ss.contents[1].text).isdigit(): 20 | year = int(ss.contents[1].text) 21 | pop_global = int(ss.contents[3].text.replace(',', '')) 22 | pop_urban = int(ss.contents[-4].text.replace(',', '')) \ 23 | if ss.contents[-4].text.replace(',', '').isdigit() else None 24 | world_population[year] = { 25 | 'pop_global': pop_global, 26 | 'pop_urban': pop_urban} 27 | print("--- {}: {}".format(year, world_population[year])) 28 | 29 | file_dir = os.path.dirname(os.path.abspath(__file__)) 30 | with open(os.path.join( 31 | file_dir, '../data/world_population.pkl'), 'wb') as handle: 32 | pickle.dump(world_population, handle, protocol=pickle.HIGHEST_PROTOCOL) 33 | -------------------------------------------------------------------------------- /scripts/extract_citations.py: -------------------------------------------------------------------------------- 1 | 2 | """ 3 | Description: Script for extracting the citations of the papers in the 4 | database. Works by sending requests to Google Scholar and 5 | scraping "Cited by" information. 6 | 7 | Issues: Google Scholar has a limit to the number of requests. 8 | I also tried DBLP, MS Academic and Semantic Scholar but could 9 | not get them to work. 10 | """ 11 | 12 | import os 13 | import re 14 | import time 15 | import pickle 16 | import requests 17 | 18 | 19 | def get_num_citations(title, authors, year, timeout=100): 20 | time.sleep(timeout) 21 | request_headers = { 22 | 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8', 23 | 'accept-encoding': 'gzip, deflate, br', 24 | 'accept-language': 'en-US,en;q=0.8', 25 | 'upgrade-insecure-requests': '1', 26 | 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36' 27 | } 28 | authors = '+'.join(['+'.join( 29 | [aa['given_name'], aa['family_name']]) for aa in authors]) 30 | req_string = 'https://scholar.google.co.uk/scholar?as_q=&as_epq={title}&as_occt=title&as_sauthors={authors}&as_publication='.format( 31 | title=title, authors=authors) 32 | html_text = requests.get(req_string, headers=request_headers).text 33 | re_result = re.search('>Cited by (.*?)', html_text) 34 | num_citations = None if re_result is None else int(re_result.group(1)) 35 | return num_citations 36 | 37 | 38 | file_dir = os.path.dirname(os.path.abspath(__file__)) 39 | with open(os.path.join( 40 | file_dir, '../data/neurips_conf_data.pkl'), 'rb') as handle: 41 | conf_data = pickle.load(handle) 42 | 43 | 44 | citation_lookup = {} 45 | for kk, vv in conf_data.items(): 46 | print("\n\nProcessing year: ", kk) 47 | for pi, paper_meta in enumerate(vv): 48 | print(" - paper [{}/{}]: {}".format( 49 | pi + 1, len(vv), paper_meta['title'])) 50 | num_cit = get_num_citations( 51 | title=paper_meta['title'], 52 | authors=paper_meta['authors'], 53 | year=paper_meta['year']) 54 | citation_lookup[paper_meta['title']] = { 55 | 'citations': num_cit 56 | } 57 | print("\t- cited by: ", num_cit) 58 | 59 | with open(os.path.join(file_dir, '../data/citations_data.pkl'), 'wb') as handle: 60 | pickle.dump(citation_lookup, handle, protocol=pickle.HIGHEST_PROTOCOL) 61 | -------------------------------------------------------------------------------- /scripts/extract_countries.py: -------------------------------------------------------------------------------- 1 | 2 | """ 3 | Description: Script for extracting the country names for the institutions 4 | in the database, by scraping location info from Wikipedia. 5 | Also finds the GDP matches from the spreadsheet. 6 | 7 | Issues: the Location info is not consistent on Wikipedia, or the 8 | institution names are not informative enough 9 | """ 10 | 11 | import os 12 | import re 13 | import pickle 14 | import requests 15 | import pandas as pd 16 | from bs4 import BeautifulSoup 17 | 18 | 19 | def get_institution_country(institution_name): 20 | search = "https://en.wikipedia.org/wiki/{}".format(institution_name) 21 | try: 22 | html_text = requests.get(search).text 23 | soup = BeautifulSoup(html_text, 'html.parser') 24 | if hasattr(soup.find( 25 | "div", attrs={'class': ['country-name']}).contents[-1], 'text'): 26 | return soup.find( 27 | "div", attrs={'class': ['country-name']}).contents[-1].text 28 | else: 29 | return str(soup.find( 30 | "div", attrs={'class': ['country-name']}).contents[-1]) 31 | except: 32 | try: 33 | if hasattr(soup.find( 34 | "span", 35 | attrs={'class': ['country-name']}).contents[-1], 'text'): 36 | return soup.find( 37 | "span", attrs={'class': ['country-name']}).contents[-1].text 38 | else: 39 | return str(soup.find( 40 | "span", attrs={'class': ['country-name']}).contents[-1]) 41 | except: 42 | try: 43 | if hasattr(soup.find( 44 | "span", 45 | attrs={'class': ['locality']}).contents[-1], 'text'): 46 | return soup.find( 47 | "span", attrs={'class': ['locality']}).contents[-1].text 48 | else: 49 | return str(soup.find( 50 | "span", attrs={'class': ['locality']}).contents[-1]) 51 | except: 52 | try: 53 | return re.search( 54 | 'class="country-name">(.*?)<', html_text).group(1) 55 | except: 56 | try: 57 | return re.search( 58 | 'class="country-name"> country: ", country) 104 | if country in country_papers.keys(): 105 | country_papers[country] += list(item.values())[0] 106 | else: 107 | country_papers[country] = list(item.values())[0] 108 | print("\nExtracted countries:\n", country_papers.keys()) 109 | with open(os.path.join(file_dir, '../data/country_papers.pkl'), 'wb') as handle: 110 | pickle.dump(country_papers, handle, protocol=pickle.HIGHEST_PROTOCOL) 111 | 112 | print("\n\nProcessing GDP:") 113 | country_gdp = {} 114 | data = pd.read_csv( 115 | os.path.join(file_dir, '../data/GDP_data.csv'), encoding='utf-8') 116 | for cc in country_papers.keys(): 117 | if len(data.index[data.iloc[:, 0] == cc]): 118 | country_gdp[cc] = float( 119 | data.iloc[data.index[(data.iloc[:, 0] == cc)], -2]) 120 | else: 121 | print("No data for", cc) 122 | with open(os.path.join(file_dir, '../data/country_gdp.pkl'), 'wb') as handle: 123 | pickle.dump(country_gdp, handle, protocol=pickle.HIGHEST_PROTOCOL) 124 | -------------------------------------------------------------------------------- /scripts/neurips_download.py: -------------------------------------------------------------------------------- 1 | 2 | """ 3 | Description: Main script for downloading the paper information from the 4 | conference website. 5 | Also tries to extract the citations (see extract_citations.py) 6 | 7 | """ 8 | 9 | import os 10 | import re 11 | import json 12 | import pickle 13 | import requests 14 | from bs4 import BeautifulSoup 15 | from urllib.parse import urljoin 16 | 17 | 18 | def get_num_citations(title, authors, year): 19 | ### Has issues, check 20 | authors = '+'.join(['+'.join([ 21 | aa['given_name'], aa['family_name']]) for aa in authors]) 22 | req_string = 'https://scholar.google.co.uk/scholar?as_q=&as_epq={title}&as_occt=title&as_sauthors={authors}&as_publication='.format( 23 | title=title, authors=authors) 24 | html_text = requests.get(req_string).text 25 | re_result = re.search('>Cited by (.*?)', html_text) 26 | num_citations = None if re_result is None else int(re_result.group(1)) 27 | return num_citations 28 | 29 | 30 | def get_reviews(html_handle): 31 | reviewer_soup = BeautifulSoup(html_handle, 'html.parser') 32 | reviewer_dict = {} 33 | for reviewer in reviewer_soup.find_all('h3'): 34 | review_text = '' 35 | for sib in reviewer.find_next_siblings(): 36 | if sib.name == "h3": 37 | break 38 | else: 39 | review_text += ' ' + sib.text 40 | re_result = re.search('Confidence in this Review (.*?)-', review_text) 41 | review_conf = None if re_result is None else int(re_result.group(1)) 42 | reviewer_dict[reviewer.contents[0]] = { 43 | 'text': review_text, 'confidence': review_conf} 44 | return reviewer_dict 45 | 46 | 47 | # Check if there is already some data 48 | if os.path.isfile('neurips_conf_data.pkl'): 49 | with open('neurips_conf_data.pkl', 'rb') as handle: 50 | conf_data = pickle.load(handle) 51 | else: 52 | conf_data = {} 53 | 54 | # Initialisation 55 | nips_papers = 'https://papers.nips.cc/' 56 | html_text = requests.get(nips_papers).text 57 | soup = BeautifulSoup(html_text, 'html.parser') 58 | 59 | 60 | # Loop through all conference years 61 | all_conferences = [ 62 | cc for cc in soup.find_all('li') if 'paper' in cc.a.get('href')] 63 | all_conferences = all_conferences[::-1] 64 | 65 | offset_conf = len(conf_data) 66 | for cc in all_conferences[offset_conf:]: 67 | conf_link = urljoin(nips_papers, cc.a.get('href')) 68 | conf_year = conf_link.split('/')[-1] 69 | html_text = requests.get(conf_link).text 70 | conf = BeautifulSoup(html_text, 'html.parser') 71 | 72 | # Loop through all current conference's papers 73 | print("\n\nProcessing: ", cc.a.contents[0]) 74 | paper_list = [] 75 | offset_papers = len(paper_list) 76 | all_papers = [ 77 | pp for pp in conf.find_all('li') if 'paper' in pp.a.get('href')] 78 | 79 | for pi, pp in enumerate(all_papers[offset_papers:]): 80 | 81 | # Get paper info 82 | print(" - paper [{}/{}]: {}".format( 83 | pi + 1 + offset_papers, len(all_papers), pp.a.contents[0])) 84 | paper_link = urljoin(conf_link, pp.a.get('href')) 85 | html_paper = requests.get(paper_link).text 86 | 87 | # Extract paper metadata 88 | if "Metadata" in html_paper: 89 | link_file = paper_link.replace('hash', 'file') 90 | link_meta = link_file.replace('html', 'json') 91 | link_meta = link_meta.replace('Abstract', 'Metadata') 92 | html_text = requests.get(link_meta).text 93 | if html_text == 'Resource Not Found': 94 | conf = BeautifulSoup(html_paper, 'html.parser') 95 | abstract_text = conf.find( 96 | 'h4', 97 | string='Abstract').next_sibling.next_sibling.contents[0] 98 | abstract = None if abstract_text == 'Abstract Unavailable' \ 99 | else abstract_text 100 | abstract = abstract.replace('

', '') 101 | abstract = abstract.replace('

', '') 102 | abstract = abstract.replace('\n', ' ') 103 | author_list = [ 104 | {'given_name': aa.split(' ')[0], 105 | 'family_name': aa.split(' ')[1], 106 | 'institution': None} 107 | for aa in pp.i.contents[0].split(', ')] 108 | paper_meta = { 109 | 'title': str(pp.a.contents[0]), 110 | 'authors': author_list, 111 | 'abstract': abstract, 112 | 'full_text': None} 113 | else: 114 | paper_meta = json.loads(html_text) 115 | if 'full_text' in paper_meta.keys(): 116 | paper_meta['full_text'] = paper_meta['full_text'].replace( 117 | '\n', ' ') 118 | else: 119 | # make nicer not copy 120 | conf = BeautifulSoup(html_paper, 'html.parser') 121 | abstract_text = conf.find( 122 | 'h4', 123 | string='Abstract').next_sibling.next_sibling.contents[0] 124 | abstract = None if abstract_text == 'Abstract Unavailable' \ 125 | else str(abstract_text) 126 | abstract = abstract.replace('

', '') 127 | abstract = abstract.replace('

', '') 128 | abstract = abstract.replace('\n', ' ') 129 | author_list = [ 130 | {'given_name': aa.split(' ')[0], 131 | 'family_name': aa.split(' ')[1], 132 | 'institution': None} for aa in pp.i.contents[0].split(', ')] 133 | paper_meta = { 134 | 'title': str(pp.a.contents[0]), 135 | 'authors': author_list, 136 | 'abstract': abstract, 137 | 'full_text': None} 138 | 139 | # Extract paper supplemental 140 | if "Supplemental" in html_paper: 141 | has_supplement = True 142 | else: 143 | has_supplement = False 144 | 145 | # Extract paper reviews 146 | if "Reviews" in html_paper: 147 | link_file = paper_link.replace('hash', 'file') 148 | link_review = link_file.replace('Abstract', 'Reviews') 149 | html_text = requests.get(link_review).text 150 | reviews = get_reviews(html_text) 151 | else: 152 | reviews = None 153 | 154 | # Extract scholar citation data 155 | num_cit = get_num_citations( 156 | title=paper_meta['title'], 157 | authors=paper_meta['authors'], 158 | year=conf_year) 159 | 160 | # Update paper info 161 | paper_meta.update({ 162 | 'year': conf_year, 163 | 'citations': num_cit, 164 | 'institutions': list( 165 | set([aa['institution'] for aa in paper_meta['authors']])), 166 | 'reviews': reviews, 167 | 'has_supplement': has_supplement}) 168 | paper_list.append(paper_meta) 169 | 170 | # Update conference info and save to file 171 | conf_data[conf_year] = paper_list 172 | with open('../data/neurips_conf_data.dat', 'wb') as handle: 173 | pickle.dump(conf_data, handle, protocol=pickle.HIGHEST_PROTOCOL) 174 | # prevents losing data 175 | os.system("mv ../data/neurips_conf_data.dat ../data/neurips_conf_data.pkl") 176 | -------------------------------------------------------------------------------- /scripts/neurips_download.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 6, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import os\n", 12 | "import re\n", 13 | "import json\n", 14 | "import pickle\n", 15 | "import requests\n", 16 | "\n", 17 | "from bs4 import BeautifulSoup\n", 18 | "from urllib.parse import urljoin\n" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 7, 24 | "metadata": { 25 | "collapsed": true 26 | }, 27 | "outputs": [], 28 | "source": [ 29 | "nips_papers = 'https://papers.nips.cc/'\n", 30 | "html_text = requests.get(nips_papers).text\n", 31 | "soup = BeautifulSoup(html_text, 'html.parser')\n" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 8, 37 | "metadata": { 38 | "collapsed": true 39 | }, 40 | "outputs": [], 41 | "source": [ 42 | "def get_num_citations(title, authors, year):\n", 43 | " ### Has issues, check\n", 44 | " ### Has issues, check\n", 45 | " authors = '+'.join(['+'.join([aa['given_name'], aa['family_name']]) for aa in authors])\n", 46 | " req_string = 'https://scholar.google.co.uk/scholar?as_q=&as_epq={title}&as_occt=title&as_sauthors={authors}&as_publication='.format(\n", 47 | " title=title, authors=authors)\n", 48 | " html_text = requests.get(req_string).text\n", 49 | " re_result = re.search('>Cited by (.*?)', html_text)\n", 50 | " num_citations = None if re_result is None else int(re_result.group(1))\n", 51 | " return num_citations\n", 52 | "\n", 53 | "def get_reviews(html_handle):\n", 54 | " reviewer_soup = BeautifulSoup(html_handle, 'html.parser')\n", 55 | " reviewer_dict = {}\n", 56 | " for reviewer in reviewer_soup.find_all('h3'):\n", 57 | " review_text = ''\n", 58 | " for sib in reviewer.find_next_siblings():\n", 59 | " if sib.name == \"h3\":\n", 60 | " break\n", 61 | " else:\n", 62 | " review_text += ' ' + sib.text\n", 63 | " re_result = re.search('Confidence in this Review (.*?)-', review_text)\n", 64 | " review_conf = None if re_result is None else int(re_result.group(1))\n", 65 | " reviewer_dict[reviewer.contents[0]] = {\n", 66 | " 'text': review_text, 'confidence': review_conf}\n", 67 | " return reviewer_dict\n", 68 | " " 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 9, 74 | "metadata": { 75 | "collapsed": true 76 | }, 77 | "outputs": [], 78 | "source": [ 79 | "# Check if there is already some data\n", 80 | "if os.path.isfile('neurips_conf_data.pkl'):\n", 81 | " with open('neurips_conf_data.pkl', 'rb') as handle:\n", 82 | " conf_data = pickle.load(handle)\n", 83 | "else:\n", 84 | " conf_data = {}\n" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 24, 90 | "metadata": { 91 | "scrolled": true 92 | }, 93 | "outputs": [ 94 | { 95 | "name": "stdout", 96 | "output_type": "stream", 97 | "text": [ 98 | "\n", 99 | "\n", 100 | "Processing: Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020)\n", 101 | " - paper [92/1898]: Blind Video Temporal Consistency via Deep Video Prior\n", 102 | " - paper [94/1898]: Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering\n", 103 | " - paper [96/1898]: Model Selection for Production System via Automated Online Experiments\n", 104 | " - paper [98/1898]: On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems\n", 105 | " - paper [100/1898]: Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond\n", 106 | " - paper [102/1898]: Adaptation Properties Allow Identification of Optimized Neural Codes\n", 107 | " - paper [104/1898]: Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems\n", 108 | " - paper [106/1898]: Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity\n", 109 | " - paper [108/1898]: Conservative Q-Learning for Offline Reinforcement Learning\n", 110 | " - paper [110/1898]: Online Influence Maximization under Linear Threshold Model\n", 111 | " - paper [112/1898]: Ensembling geophysical models with Bayesian Neural Networks\n", 112 | " - paper [114/1898]: Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation\n", 113 | " - paper [116/1898]: Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability\n", 114 | " - paper [118/1898]: Understanding Deep Architecture with Reasoning Layer\n", 115 | " - paper [120/1898]: Planning in Markov Decision Processes with Gap-Dependent Sample Complexity\n", 116 | " - paper [122/1898]: Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration\n", 117 | " - paper [124/1898]: Detection as Regression: Certified Object Detection with Median Smoothing\n", 118 | " - paper [126/1898]: Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming\n", 119 | " - paper [128/1898]: ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks\n", 120 | " - paper [130/1898]: FleXOR: Trainable Fractional Quantization\n", 121 | " - paper [132/1898]: The Implications of Local Correlation on Learning Some Deep Functions\n", 122 | " - paper [134/1898]: Learning to search efficiently for causally near-optimal treatments\n", 123 | " - paper [136/1898]: A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses\n", 124 | " - paper [138/1898]: Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts\n", 125 | " - paper [140/1898]: Recurrent Quantum Neural Networks\n", 126 | " - paper [142/1898]: No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix\n", 127 | " - paper [144/1898]: A Unifying View of Optimism in Episodic Reinforcement Learning\n", 128 | " - paper [146/1898]: Continuous Submodular Maximization: Beyond DR-Submodularity\n", 129 | " - paper [148/1898]: An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits\n", 130 | " - paper [150/1898]: Assessing SATNet's Ability to Solve the Symbol Grounding Problem\n", 131 | " - paper [152/1898]: A Bayesian Nonparametrics View into Deep Representations\n", 132 | " - paper [154/1898]: On the Similarity between the Laplace and Neural Tangent Kernels\n", 133 | " - paper [156/1898]: A causal view of compositional zero-shot recognition\n", 134 | " - paper [158/1898]: HiPPO: Recurrent Memory with Optimal Polynomial Projections\n", 135 | " - paper [160/1898]: Auto Learning Attention\n", 136 | " - paper [162/1898]: CASTLE: Regularization via Auxiliary Causal Graph Discovery\n", 137 | " - paper [164/1898]: Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect\n", 138 | " - paper [166/1898]: Explainable Voting\n", 139 | " - paper [168/1898]: Deep Archimedean Copulas\n", 140 | " - paper [170/1898]: Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization\n", 141 | " - paper [172/1898]: UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging\n", 142 | " - paper [174/1898]: Thunder: a Fast Coordinate Selection Solver for Sparse Learning\n", 143 | " - paper [176/1898]: Neural Networks Fail to Learn Periodic Functions and How to Fix It\n", 144 | " - paper [178/1898]: Distribution Matching for Crowd Counting\n", 145 | " - paper [180/1898]: Correspondence learning via linearly-invariant embedding\n", 146 | " - paper [182/1898]: Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning\n", 147 | " - paper [184/1898]: On Adaptive Attacks to Adversarial Example Defenses\n", 148 | " - paper [186/1898]: Sinkhorn Natural Gradient for Generative Models\n", 149 | " - paper [188/1898]: Online Sinkhorn: Optimal Transport distances from sample streams\n", 150 | " - paper [190/1898]: Ultrahyperbolic Representation Learning\n", 151 | " - paper [192/1898]: Locally-Adaptive Nonparametric Online Learning\n", 152 | " - paper [194/1898]: Compositional Generalization via Neural-Symbolic Stack Machines\n", 153 | " - paper [196/1898]: Graphon Neural Networks and the Transferability of Graph Neural Networks\n", 154 | " - paper [198/1898]: Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms\n", 155 | " - paper [200/1898]: Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction\n", 156 | " - paper [202/1898]: Deep Transformers with Latent Depth\n", 157 | " - paper [204/1898]: Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows\n", 158 | " - paper [206/1898]: Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso\n", 159 | " - paper [208/1898]: A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees\n", 160 | " - paper [210/1898]: Efficient Exact Verification of Binarized Neural Networks\n", 161 | " - paper [212/1898]: Ultra-Low Precision 4-bit Training of Deep Neural Networks\n", 162 | " - paper [214/1898]: Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS\n", 163 | " - paper [216/1898]: On Numerosity of Deep Neural Networks\n", 164 | " - paper [218/1898]: Outlier Robust Mean Estimation with Subgaussian Rates via Stability\n", 165 | " - paper [220/1898]: Self-Supervised Relationship Probing\n", 166 | " - paper [222/1898]: Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback\n", 167 | " - paper [224/1898]: Prophet Attention: Predicting Attention with Future Attention\n", 168 | " - paper [226/1898]: Language Models are Few-Shot Learners\n", 169 | " - paper [228/1898]: Margins are Insufficient for Explaining Gradient Boosting\n", 170 | " - paper [230/1898]: Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics\n", 171 | " - paper [232/1898]: MomentumRNN: Integrating Momentum into Recurrent Neural Networks\n", 172 | " - paper [234/1898]: Marginal Utility for Planning in Continuous or Large Discrete Action Spaces\n", 173 | " - paper [236/1898]: Projected Stein Variational Gradient Descent\n", 174 | " - paper [238/1898]: Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks\n", 175 | " - paper [240/1898]: SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks\n", 176 | " - paper [242/1898]: On the equivalence of molecular graph convolution and molecular wave function with poor basis set\n", 177 | " - paper [244/1898]: The Power of Predictions in Online Control\n", 178 | " - paper [246/1898]: Learning Affordance Landscapes for Interaction Exploration in 3D Environments\n", 179 | " - paper [248/1898]: Cooperative Multi-player Bandit Optimization \n", 180 | " - paper [250/1898]: Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits\n", 181 | " - paper [252/1898]: Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout\n", 182 | " - paper [254/1898]: A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model\n", 183 | " - paper [256/1898]: Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains\n", 184 | " - paper [258/1898]: Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward\n", 185 | " - paper [260/1898]: Optimizing Neural Networks via Koopman Operator Theory\n", 186 | " - paper [262/1898]: SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence\n", 187 | " - paper [264/1898]: Adversarial Robustness of Supervised Sparse Coding\n", 188 | " - paper [266/1898]: Differentiable Meta-Learning of Bandit Policies\n", 189 | " - paper [268/1898]: Biologically Inspired Mechanisms for Adversarial Robustness\n", 190 | " - paper [270/1898]: Statistical-Query Lower Bounds via Functional Gradients\n", 191 | " - paper [272/1898]: Near-Optimal Reinforcement Learning with Self-Play\n", 192 | " - paper [274/1898]: Network Diffusions via Neural Mean-Field Dynamics\n", 193 | " - paper [276/1898]: Self-Distillation as Instance-Specific Label Smoothing\n", 194 | " - paper [278/1898]: Towards Problem-dependent Optimal Learning Rates\n" 195 | ] 196 | }, 197 | { 198 | "name": "stdout", 199 | "output_type": "stream", 200 | "text": [ 201 | " - paper [280/1898]: Cross-lingual Retrieval for Iterative Self-Supervised Training\n", 202 | " - paper [282/1898]: Rethinking pooling in graph neural networks\n", 203 | " - paper [284/1898]: Pointer Graph Networks\n", 204 | " - paper [286/1898]: Gradient Regularized V-Learning for Dynamic Treatment Regimes\n", 205 | " - paper [288/1898]: Faster Wasserstein Distance Estimation with the Sinkhorn Divergence\n", 206 | " - paper [290/1898]: Forethought and Hindsight in Credit Assignment\n", 207 | " - paper [292/1898]: Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification\n", 208 | " - paper [294/1898]: Rescuing neural spike train models from bad MLE\n", 209 | " - paper [296/1898]: Lower Bounds and Optimal Algorithms for Personalized Federated Learning\n", 210 | " - paper [298/1898]: Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework\n", 211 | " - paper [300/1898]: Deep Imitation Learning for Bimanual Robotic Manipulation\n", 212 | " - paper [302/1898]: Stationary Activations for Uncertainty Calibration in Deep Learning\n", 213 | " - paper [304/1898]: Ensemble Distillation for Robust Model Fusion in Federated Learning\n", 214 | " - paper [306/1898]: Falcon: Fast Spectral Inference on Encrypted Data\n", 215 | " - paper [308/1898]: On Power Laws in Deep Ensembles\n", 216 | " - paper [310/1898]: Practical Quasi-Newton Methods for Training Deep Neural Networks\n", 217 | " - paper [312/1898]: Approximation Based Variance Reduction for Reparameterization Gradients\n", 218 | " - paper [314/1898]: Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation\n", 219 | " - paper [316/1898]: Consistent feature selection for analytic deep neural networks\n", 220 | " - paper [318/1898]: Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification\n", 221 | " - paper [320/1898]: Information Maximization for Few-Shot Learning\n", 222 | " - paper [322/1898]: Inverse Reinforcement Learning from a Gradient-based Learner\n", 223 | " - paper [324/1898]: Bayesian Multi-type Mean Field Multi-agent Imitation Learning\n", 224 | " - paper [326/1898]: Bayesian Robust Optimization for Imitation Learning\n", 225 | " - paper [328/1898]: Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance\n", 226 | " - paper [330/1898]: Riemannian Continuous Normalizing Flows\n", 227 | " - paper [332/1898]: Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation\n", 228 | " - paper [334/1898]: Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance\n", 229 | " - paper [336/1898]: Online Robust Regression via SGD on the l1 loss\n", 230 | " - paper [338/1898]: PRANK: motion Prediction based on RANKing\n", 231 | " - paper [340/1898]: Fighting Copycat Agents in Behavioral Cloning from Observation Histories\n", 232 | " - paper [342/1898]: Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model\n", 233 | " - paper [344/1898]: Structured Prediction for Conditional Meta-Learning\n", 234 | " - paper [346/1898]: Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient\n", 235 | " - paper [348/1898]: The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes\n", 236 | " - paper [350/1898]: Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function\n", 237 | " - paper [352/1898]: Identifying Learning Rules From Neural Network Observables\n", 238 | " - paper [354/1898]: Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions\n", 239 | " - paper [356/1898]: Weakly-Supervised Reinforcement Learning for Controllable Behavior\n", 240 | " - paper [358/1898]: Improving Policy-Constrained Kidney Exchange via Pre-Screening\n", 241 | " - paper [360/1898]: Learning abstract structure for drawing by efficient motor program induction\n", 242 | " - paper [362/1898]: Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective\n", 243 | " - paper [364/1898]: Dual Instrumental Variable Regression\n", 244 | " - paper [366/1898]: Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes\n", 245 | " - paper [368/1898]: Interventional Few-Shot Learning\n", 246 | " - paper [370/1898]: Minimax Value Interval for Off-Policy Evaluation and Policy Optimization\n", 247 | " - paper [372/1898]: Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning\n", 248 | " - paper [374/1898]: ShiftAddNet: A Hardware-Inspired Deep Network\n", 249 | " - paper [376/1898]: Network-to-Network Translation with Conditional Invertible Neural Networks\n", 250 | " - paper [378/1898]: Intra-Processing Methods for Debiasing Neural Networks\n", 251 | " - paper [380/1898]: Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems\n", 252 | " - paper [382/1898]: Model-based Policy Optimization with Unsupervised Model Adaptation\n", 253 | " - paper [384/1898]: Implicit Regularization and Convergence for Weight Normalization\n", 254 | " - paper [386/1898]: Geometric All-way Boolean Tensor Decomposition\n", 255 | " - paper [388/1898]: Modular Meta-Learning with Shrinkage\n", 256 | " - paper [390/1898]: A/B Testing in Dense Large-Scale Networks: Design and Inference\n", 257 | " - paper [392/1898]: What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation\n", 258 | " - paper [394/1898]: Partially View-aligned Clustering\n", 259 | " - paper [396/1898]: Partial Optimal Tranport with applications on Positive-Unlabeled Learning\n", 260 | " - paper [398/1898]: Toward the Fundamental Limits of Imitation Learning\n", 261 | " - paper [400/1898]: Logarithmic Pruning is All You Need\n", 262 | " - paper [402/1898]: Hold me tight! Influence of discriminative features on deep network boundaries\n", 263 | " - paper [404/1898]: Learning from Mixtures of Private and Public Populations\n", 264 | " - paper [406/1898]: Adversarial Weight Perturbation Helps Robust Generalization\n", 265 | " - paper [408/1898]: Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes\n", 266 | " - paper [410/1898]: Adversarial Self-Supervised Contrastive Learning\n", 267 | " - paper [412/1898]: Normalizing Kalman Filters for Multivariate Time Series Analysis\n", 268 | " - paper [414/1898]: Learning to summarize with human feedback\n", 269 | " - paper [416/1898]: Fourier Spectrum Discrepancies in Deep Network Generated Images\n", 270 | " - paper [418/1898]: Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks\n", 271 | " - paper [420/1898]: Learning Dynamic Belief Graphs to Generalize on Text-Based Games\n", 272 | " - paper [422/1898]: Triple descent and the two kinds of overfitting: where & why do they appear?\n", 273 | " - paper [424/1898]: Multimodal Graph Networks for Compositional Generalization in Visual Question Answering\n", 274 | " - paper [426/1898]: Learning Graph Structure With A Finite-State Automaton Layer\n", 275 | " - paper [428/1898]: A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions\n", 276 | " - paper [430/1898]: Unsupervised object-centric video generation and decomposition in 3D\n", 277 | " - paper [432/1898]: Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization\n", 278 | " - paper [434/1898]: Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?\n", 279 | " - paper [436/1898]: A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances\n", 280 | " - paper [438/1898]: Causal analysis of Covid-19 Spread in Germany\n", 281 | " - paper [440/1898]: Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms\n", 282 | " - paper [442/1898]: Adaptive Gradient Quantization for Data-Parallel SGD\n", 283 | " - paper [444/1898]: Finite Continuum-Armed Bandits\n", 284 | " - paper [446/1898]: Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies\n", 285 | " - paper [448/1898]: Compact task representations as a normative model for higher-order brain activity\n", 286 | " - paper [450/1898]: Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs\n", 287 | " - paper [452/1898]: Co-exposure Maximization in Online Social Networks\n", 288 | " - paper [454/1898]: UCLID-Net: Single View Reconstruction in Object Space\n", 289 | " - paper [456/1898]: Reinforcement Learning for Control with Multiple Frequencies\n", 290 | " - paper [458/1898]: Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval\n", 291 | " - paper [460/1898]: Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs\n", 292 | " - paper [462/1898]: A Unified View of Label Shift Estimation\n", 293 | " - paper [464/1898]: Optimal Private Median Estimation under Minimal Distributional Assumptions\n" 294 | ] 295 | }, 296 | { 297 | "name": "stdout", 298 | "output_type": "stream", 299 | "text": [ 300 | " - paper [466/1898]: Breaking the Communication-Privacy-Accuracy Trilemma\n", 301 | " - paper [468/1898]: Audeo: Audio Generation for a Silent Performance Video\n", 302 | " - paper [470/1898]: Ode to an ODE\n", 303 | " - paper [472/1898]: Self-Distillation Amplifies Regularization in Hilbert Space\n", 304 | " - paper [474/1898]: Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators\n", 305 | " - paper [476/1898]: Community detection using fast low-cardinality semidefinite programming
", 306 | "\n", 307 | " - paper [478/1898]: Modeling Noisy Annotations for Crowd Counting\n", 308 | " - paper [480/1898]: An operator view of policy gradient methods\n", 309 | " - paper [482/1898]: Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases\n", 310 | " - paper [484/1898]: Online MAP Inference of Determinantal Point Processes\n", 311 | " - paper [486/1898]: Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement\n", 312 | " - paper [488/1898]: Inferring learning rules from animal decision-making\n", 313 | " - paper [490/1898]: Input-Aware Dynamic Backdoor Attack\n", 314 | " - paper [492/1898]: How hard is to distinguish graphs with graph neural networks?\n", 315 | " - paper [494/1898]: Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition\n", 316 | " - paper [496/1898]: Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks\n", 317 | " - paper [498/1898]: Cross-Scale Internal Graph Neural Network for Image Super-Resolution\n", 318 | " - paper [500/1898]: Unsupervised Representation Learning by Invariance Propagation\n", 319 | " - paper [502/1898]: Restoring Negative Information in Few-Shot Object Detection\n", 320 | " - paper [504/1898]: Do Adversarially Robust ImageNet Models Transfer Better?\n", 321 | " - paper [506/1898]: Robust Correction of Sampling Bias using Cumulative Distribution Functions\n", 322 | " - paper [508/1898]: Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach\n", 323 | " - paper [510/1898]: Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation\n", 324 | " - paper [512/1898]: Classification with Valid and Adaptive Coverage\n", 325 | " - paper [514/1898]: Learning Global Transparent Models consistent with Local Contrastive Explanations\n", 326 | " - paper [516/1898]: Learning to Approximate a Bregman Divergence\n", 327 | " - paper [518/1898]: Diverse Image Captioning with Context-Object Split Latent Spaces\n", 328 | " - paper [520/1898]: Learning Disentangled Representations of Videos with Missing Data\n", 329 | " - paper [522/1898]: Natural Graph Networks\n", 330 | " - paper [524/1898]: Continual Learning with Node-Importance based Adaptive Group Sparse Regularization\n", 331 | " - paper [526/1898]: Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts\n", 332 | " - paper [528/1898]: Bidirectional Convolutional Poisson Gamma Dynamical Systems\n", 333 | " - paper [530/1898]: Deep Reinforcement and InfoMax Learning\n", 334 | " - paper [532/1898]: On ranking via sorting by estimated expected utility\n", 335 | " - paper [534/1898]: Distribution-free binary classification: prediction sets, confidence intervals and calibration\n", 336 | " - paper [536/1898]: Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow\n", 337 | " - paper [538/1898]: Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals\n", 338 | " - paper [540/1898]: Variance reduction for Random Coordinate Descent-Langevin Monte Carlo\n", 339 | " - paper [542/1898]: Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration\n", 340 | " - paper [544/1898]: All Word Embeddings from One Embedding\n", 341 | " - paper [546/1898]: Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm\n", 342 | " - paper [548/1898]: How to Characterize The Landscape of Overparameterized Convolutional Neural Networks\n", 343 | " - paper [550/1898]: On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples\n", 344 | " - paper [552/1898]: Submodular Meta-Learning\n", 345 | " - paper [554/1898]: Rethinking Pre-training and Self-training\n", 346 | " - paper [556/1898]: Unsupervised Sound Separation Using Mixture Invariant Training\n", 347 | " - paper [558/1898]: Adaptive Discretization for Model-Based Reinforcement Learning\n", 348 | " - paper [560/1898]: CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching\n", 349 | " - paper [562/1898]: On Warm-Starting Neural Network Training\n", 350 | " - paper [564/1898]: DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks\n", 351 | " - paper [566/1898]: OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification\n", 352 | " - paper [568/1898]: An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch\n", 353 | " - paper [570/1898]: Learning About Objects by Learning to Interact with Them\n", 354 | " - paper [572/1898]: Learning discrete distributions with infinite support\n", 355 | " - paper [574/1898]: Dissecting Neural ODEs\n", 356 | " - paper [576/1898]: Teaching a GAN What Not to Learn\n", 357 | " - paper [578/1898]: Counterfactual Data Augmentation using Locally Factored Dynamics\n", 358 | " - paper [580/1898]: Rethinking Learnable Tree Filter for Generic Feature Transform\n", 359 | " - paper [582/1898]: Self-Supervised Relational Reasoning for Representation Learning\n", 360 | " - paper [584/1898]: Sufficient dimension reduction for classification using principal optimal transport direction\n", 361 | " - paper [586/1898]: Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine\n", 362 | " - paper [588/1898]: Differentially Private Clustering: Tight Approximation Ratios\n", 363 | " - paper [590/1898]: On the Power of Louvain in the Stochastic Block Model\n", 364 | " - paper [592/1898]: Fairness with Overlapping Groups; a Probabilistic Perspective\n", 365 | " - paper [594/1898]: AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control\n", 366 | " - paper [596/1898]: Searching for Low-Bit Weights in Quantized Neural Networks\n", 367 | " - paper [598/1898]: Adaptive Reduced Rank Regression\n", 368 | " - paper [600/1898]: From Predictions to Decisions: Using Lookahead Regularization\n", 369 | " - paper [602/1898]: Sequential Bayesian Experimental Design with Variable Cost Structure\n", 370 | " - paper [604/1898]: Predictive inference is free with the jackknife+-after-bootstrap\n", 371 | " - paper [606/1898]: Counterfactual Predictions under Runtime Confounding\n", 372 | " - paper [608/1898]: Learning Loss for Test-Time Augmentation\n", 373 | " - paper [610/1898]: Balanced Meta-Softmax for Long-Tailed Visual Recognition\n", 374 | " - paper [612/1898]: Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization\n", 375 | " - paper [614/1898]: MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning\n", 376 | " - paper [616/1898]: How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods\n", 377 | " - paper [618/1898]: On the Error Resistance of Hinge-Loss Minimization\n", 378 | " - paper [620/1898]: Munchausen Reinforcement Learning\n", 379 | " - paper [622/1898]: Object Goal Navigation using Goal-Oriented Semantic Exploration\n", 380 | " - paper [624/1898]: Efficient semidefinite-programming-based inference for binary and multi-class MRFs\n", 381 | " - paper [626/1898]: Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing\n", 382 | " - paper [628/1898]: Semantic Visual Navigation by Watching YouTube Videos\n", 383 | " - paper [630/1898]: Heavy-tailed Representations, Text Polarity Classification & Data Augmentation\n", 384 | " - paper [632/1898]: SuperLoss: A Generic Loss for Robust Curriculum Learning\n", 385 | " - paper [634/1898]: CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models\n", 386 | " - paper [636/1898]: Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards\n", 387 | " - paper [638/1898]: Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations\n", 388 | " - paper [640/1898]: Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms\n", 389 | " - paper [642/1898]: Learning Differential Equations that are Easy to Solve\n", 390 | " - paper [644/1898]: Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses\n", 391 | " - paper [646/1898]: Influence-Augmented Online Planning for Complex Environments\n", 392 | " - paper [648/1898]: PAC-Bayes Learning Bounds for Sample-Dependent Priors\n", 393 | " - paper [650/1898]: Reward-rational (implicit) choice: A unifying formalism for reward learning\n", 394 | " - paper [652/1898]: Probabilistic Time Series Forecasting with Shape and Temporal Diversity\n" 395 | ] 396 | }, 397 | { 398 | "name": "stdout", 399 | "output_type": "stream", 400 | "text": [ 401 | " - paper [654/1898]: Low Distortion Block-Resampling with Spatially Stochastic Networks\n", 402 | " - paper [656/1898]: Continual Deep Learning by Functional Regularisation of Memorable Past\n", 403 | " - paper [658/1898]: Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning\n", 404 | " - paper [660/1898]: Fast Fourier Convolution\n", 405 | " - paper [662/1898]: Unsupervised Learning of Dense Visual Representations\n", 406 | " - paper [664/1898]: Higher-Order Certification For Randomized Smoothing\n", 407 | " - paper [666/1898]: Learning Structured Distributions From Untrusted Batches: Faster and Simpler\n", 408 | " - paper [668/1898]: Hierarchical Quantized Autoencoders\n", 409 | " - paper [670/1898]: Diversity can be Transferred: Output Diversification for White- and Black-box Attacks\n", 410 | " - paper [672/1898]: POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis\n", 411 | " - paper [674/1898]: AvE: Assistance via Empowerment\n", 412 | " - paper [676/1898]: Variational Policy Gradient Method for Reinforcement Learning with General Utilities\n", 413 | " - paper [678/1898]: Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice\n", 414 | " - paper [680/1898]: Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation\n", 415 | " - paper [682/1898]: Efficient Low Rank Gaussian Variational Inference for Neural Networks\n", 416 | " - paper [684/1898]: Privacy Amplification via Random Check-Ins\n", 417 | " - paper [686/1898]: Probabilistic Circuits for Variational Inference in Discrete Graphical Models\n", 418 | " - paper [688/1898]: Your Classifier can Secretly Suffice Multi-Source Domain Adaptation\n", 419 | " - paper [690/1898]: Labelling unlabelled videos from scratch with multi-modal self-supervision\n", 420 | " - paper [692/1898]: A Non-Asymptotic Analysis for Stein Variational Gradient Descent\n", 421 | " - paper [694/1898]: Robust Meta-learning for Mixed Linear Regression with Small Batches\n", 422 | " - paper [696/1898]: Bayesian Deep Learning and a Probabilistic Perspective of Generalization\n", 423 | " - paper [698/1898]: Unsupervised Learning of Object Landmarks via Self-Training Correspondence\n", 424 | " - paper [700/1898]: Randomized tests for high-dimensional regression: A more efficient and powerful solution\n", 425 | " - paper [702/1898]: Learning Representations from Audio-Visual Spatial Alignment\n", 426 | " - paper [704/1898]: Generative View Synthesis: From Single-view Semantics to Novel-view Images\n", 427 | " - paper [706/1898]: Towards More Practical Adversarial Attacks on Graph Neural Networks\n", 428 | " - paper [708/1898]: Multi-Task Reinforcement Learning with Soft Modularization\n", 429 | " - paper [710/1898]: Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models\n", 430 | " - paper [712/1898]: On the training dynamics of deep networks with $L_2$ regularization\n", 431 | " - paper [714/1898]: Improved Algorithms for Convex-Concave Minimax Optimization\n", 432 | " - paper [716/1898]: Deep Variational Instance Segmentation\n", 433 | " - paper [718/1898]: Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence\n", 434 | " - paper [720/1898]: Deep Multimodal Fusion by Channel Exchanging\n", 435 | " - paper [722/1898]: Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems\n", 436 | " - paper [724/1898]: AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity\n", 437 | " - paper [726/1898]: Delay and Cooperation in Nonstochastic Linear Bandits\n", 438 | " - paper [728/1898]: Probabilistic Orientation Estimation with Matrix Fisher Distributions\n", 439 | " - paper [730/1898]: Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons\n", 440 | " - paper [732/1898]: Telescoping Density-Ratio Estimation\n", 441 | " - paper [734/1898]: Towards Deeper Graph Neural Networks with Differentiable Group Normalization\n", 442 | " - paper [736/1898]: Stochastic Optimization for Performative Prediction\n", 443 | " - paper [738/1898]: Learning Differentiable Programs with Admissible Neural Heuristics\n", 444 | " - paper [740/1898]: Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method\n", 445 | " - paper [742/1898]: Domain Adaptation as a Problem of Inference on Graphical Models\n", 446 | " - paper [744/1898]: Network size and size of the weights in memorization with two-layers neural networks\n", 447 | " - paper [746/1898]: Certifying Strategyproof Auction Networks\n", 448 | " - paper [748/1898]: Continual Learning of Control Primitives : Skill Discovery via Reset-Games\n", 449 | " - paper [750/1898]: HOI Analysis: Integrating and Decomposing Human-Object Interaction\n", 450 | " - paper [752/1898]: Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering\n", 451 | " - paper [754/1898]: Deep Direct Likelihood Knockoffs\n", 452 | " - paper [756/1898]: Meta-Neighborhoods\n", 453 | " - paper [758/1898]: Neural Dynamic Policies for End-to-End Sensorimotor Learning\n", 454 | " - paper [760/1898]: A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons\n", 455 | " - paper [762/1898]: Decision-Making with Auto-Encoding Variational Bayes\n", 456 | " - paper [764/1898]: Attribution Preservation in Network Compression for Reliable Network Interpretation\n", 457 | " - paper [766/1898]: Feature Importance Ranking for Deep Learning\n", 458 | " - paper [768/1898]: Causal Estimation with Functional Confounders\n", 459 | " - paper [770/1898]: Model Inversion Networks for Model-Based Optimization\n", 460 | " - paper [772/1898]: Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks\n", 461 | " - paper [774/1898]: Exact expressions for double descent and implicit regularization via surrogate random design\n", 462 | " - paper [776/1898]: Certifying Confidence via Randomized Smoothing\n", 463 | " - paper [778/1898]: Learning Physical Constraints with Neural Projections\n", 464 | " - paper [780/1898]: Robust Optimization for Fairness with Noisy Protected Groups\n", 465 | " - paper [782/1898]: Noise-Contrastive Estimation for Multivariate Point Processes\n", 466 | " - paper [784/1898]: A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling\n", 467 | " - paper [786/1898]: Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning\n", 468 | " - paper [788/1898]: Multiscale Deep Equilibrium Models\n", 469 | " - paper [790/1898]: Sparse Graphical Memory for Robust Planning\n", 470 | " - paper [792/1898]: Second Order PAC-Bayesian Bounds for the Weighted Majority Vote\n", 471 | " - paper [794/1898]: Dirichlet Graph Variational Autoencoder\n", 472 | " - paper [796/1898]: Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction\n", 473 | " - paper [798/1898]: Counterfactual Vision-and-Language Navigation: Unravelling the Unseen\n", 474 | " - paper [800/1898]: Robust Quantization: One Model to Rule Them All\n", 475 | " - paper [802/1898]: Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming\n", 476 | " - paper [804/1898]: Federated Accelerated Stochastic Gradient Descent\n", 477 | " - paper [806/1898]: Robust Density Estimation under Besov IPM Losses\n", 478 | " - paper [808/1898]: An analytic theory of shallow networks dynamics for hinge loss classification\n", 479 | " - paper [810/1898]: Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm\n", 480 | " - paper [812/1898]: Learning to Orient Surfaces by Self-supervised Spherical CNNs\n", 481 | " - paper [814/1898]: Adam with Bandit Sampling for Deep Learning\n", 482 | " - paper [816/1898]: Parabolic Approximation Line Search for DNNs\n", 483 | " - paper [818/1898]: Agnostic Learning of a Single Neuron with Gradient Descent\n", 484 | " - paper [820/1898]: Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits\n", 485 | " - paper [822/1898]: Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry\n", 486 | " - paper [824/1898]: Generative causal explanations of black-box classifiers\n", 487 | " - paper [826/1898]: Sub-sampling for Efficient Non-Parametric Bandit Exploration\n", 488 | " - paper [828/1898]: Learning under Model Misspecification: Applications to Variational and Ensemble methods\n", 489 | " - paper [830/1898]: Language Through a Prism: A Spectral Approach for Multiscale Language Representations\n", 490 | " - paper [832/1898]: DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles\n", 491 | " - paper [834/1898]: Towards practical differentially private causal graph discovery\n", 492 | " - paper [836/1898]: Independent Policy Gradient Methods for Competitive Reinforcement Learning\n", 493 | " - paper [838/1898]: The Value Equivalence Principle for Model-Based Reinforcement Learning\n" 494 | ] 495 | }, 496 | { 497 | "name": "stdout", 498 | "output_type": "stream", 499 | "text": [ 500 | " - paper [840/1898]: Structured Convolutions for Efficient Neural Network Design\n", 501 | " - paper [842/1898]: Latent World Models For Intrinsically Motivated Exploration\n", 502 | " - paper [844/1898]: Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks\n", 503 | " - paper [846/1898]: Policy Improvement via Imitation of Multiple Oracles\n", 504 | " - paper [848/1898]: Training Generative Adversarial Networks by Solving Ordinary Differential Equations\n", 505 | " - paper [850/1898]: Learning of Discrete Graphical Models with Neural Networks\n", 506 | " - paper [852/1898]: RepPoints v2: Verification Meets Regression for Object Detection\n", 507 | " - paper [854/1898]: Unfolding the Alternating Optimization for Blind Super Resolution\n", 508 | " - paper [856/1898]: Entrywise convergence of iterative methods for eigenproblems\n", 509 | " - paper [858/1898]: Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views\n", 510 | " - paper [860/1898]: A Catalyst Framework for Minimax Optimization\n", 511 | " - paper [862/1898]: Self-supervised Co-Training for Video Representation Learning\n", 512 | " - paper [864/1898]: Gradient Estimation with Stochastic Softmax Tricks\n", 513 | " - paper [866/1898]: Meta-Learning Requires Meta-Augmentation\n", 514 | " - paper [868/1898]: SLIP: Learning to predict in unknown dynamical systems with long-term memory\n", 515 | " - paper [870/1898]: Improving GAN Training with Probability Ratio Clipping and Sample Reweighting\n", 516 | " - paper [872/1898]: Bayesian Bits: Unifying Quantization and Pruning\n", 517 | " - paper [874/1898]: On Testing of Samplers\n", 518 | " - paper [876/1898]: Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective\n", 519 | " - paper [878/1898]: MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers\n", 520 | " - paper [880/1898]: Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization\n", 521 | " - paper [882/1898]: Woodbury Transformations for Deep Generative Flows\n", 522 | " - paper [884/1898]: Graph Contrastive Learning with Augmentations\n", 523 | " - paper [886/1898]: Gradient Surgery for Multi-Task Learning\n", 524 | " - paper [888/1898]: Bayesian Probabilistic Numerical Integration with Tree-Based Models\n", 525 | " - paper [890/1898]: Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel\n", 526 | " - paper [892/1898]: Graph Meta Learning via Local Subgraphs\n", 527 | " - paper [894/1898]: Stochastic Deep Gaussian Processes over Graphs\n", 528 | " - paper [896/1898]: Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks\n", 529 | " - paper [898/1898]: Evaluating Attribution for Graph Neural Networks\n", 530 | " - paper [900/1898]: On Second Order Behaviour in Augmented Neural ODEs\n", 531 | " - paper [902/1898]: Neuron Shapley: Discovering the Responsible Neurons\n", 532 | " - paper [904/1898]: Stochastic Normalizing Flows\n", 533 | " - paper [906/1898]: GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification\n", 534 | " - paper [908/1898]: Random Reshuffling is Not Always Better\n", 535 | " - paper [910/1898]: Model Agnostic Multilevel Explanations\n", 536 | " - paper [912/1898]: NeuMiss networks: differentiable programming for supervised learning with missing values.\n", 537 | " - paper [914/1898]: Revisiting Parameter Sharing for Automatic Neural Channel Number Search\n", 538 | " - paper [916/1898]: Differentially-Private Federated Linear Bandits\n", 539 | " - paper [918/1898]: Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?\n", 540 | " - paper [920/1898]: Learning Physical Graph Representations from Visual Scenes\n", 541 | " - paper [922/1898]: Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking\n", 542 | " - paper [924/1898]: Meta-learning from Tasks with Heterogeneous Attribute Spaces\n", 543 | " - paper [926/1898]: Estimating decision tree learnability with polylogarithmic sample complexity\n", 544 | " - paper [928/1898]: Sparse Symplectically Integrated Neural Networks\n", 545 | " - paper [930/1898]: Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision\n", 546 | " - paper [932/1898]: Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence\n", 547 | " - paper [934/1898]: Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers\n", 548 | " - paper [936/1898]: Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension\n", 549 | " - paper [938/1898]: Predicting Training Time Without Training \n", 550 | " - paper [940/1898]: How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions\n", 551 | " - paper [942/1898]: Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield\n", 552 | " - paper [944/1898]: Neurosymbolic Reinforcement Learning with Formally Verified Exploration\n", 553 | " - paper [946/1898]: Wavelet Flow: Fast Training of High Resolution Normalizing Flows\n", 554 | " - paper [948/1898]: Multi-task Batch Reinforcement Learning with Metric Learning\n", 555 | " - paper [950/1898]: On 1/n neural representation and robustness\n", 556 | " - paper [952/1898]: Boundary thickness and robustness in learning models\n", 557 | " - paper [954/1898]: Demixed shared component analysis of neural population data from multiple brain areas\n", 558 | " - paper [956/1898]: Learning Kernel Tests Without Data Splitting\n", 559 | " - paper [958/1898]: Unsupervised Data Augmentation for Consistency Training\n", 560 | " - paper [960/1898]: Subgroup-based Rank-1 Lattice Quasi-Monte Carlo\n", 561 | " - paper [962/1898]: Minibatch vs Local SGD for Heterogeneous Distributed Learning\n", 562 | " - paper [964/1898]: Multi-task Causal Learning with Gaussian Processes\n", 563 | " - paper [966/1898]: Proximity Operator of the Matrix Perspective Function and its Applications\n", 564 | " - paper [968/1898]: Generative 3D Part Assembly via Dynamic Graph Learning\n", 565 | " - paper [970/1898]: Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention\n", 566 | " - paper [972/1898]: The Power of Comparisons for Actively Learning Linear Classifiers\n", 567 | " - paper [974/1898]: From Boltzmann Machines to Neural Networks and Back Again\n", 568 | " - paper [976/1898]: Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality\n", 569 | " - paper [978/1898]: Pruning neural networks without any data by iteratively conserving synaptic flow\n", 570 | " - paper [980/1898]: Detecting Interactions from Neural Networks via Topological Analysis\n", 571 | " - paper [982/1898]: Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems\n", 572 | " - paper [984/1898]: Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations\n", 573 | " - paper [986/1898]: Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes\n", 574 | " - paper [988/1898]: Benchmarking Deep Learning Interpretability in Time Series Predictions\n", 575 | " - paper [990/1898]: Federated Principal Component Analysis\n", 576 | " - paper [992/1898]: (De)Randomized Smoothing for Certifiable Defense against Patch Attacks\n", 577 | " - paper [994/1898]: SMYRF - Efficient Attention using Asymmetric Clustering\n", 578 | " - paper [996/1898]: Introducing Routing Uncertainty in Capsule Networks\n", 579 | " - paper [998/1898]: A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration\n", 580 | " - paper [1000/1898]: Hyperparameter Ensembles for Robustness and Uncertainty Quantification\n", 581 | " - paper [1002/1898]: Neutralizing Self-Selection Bias in Sampling for Sortition\n", 582 | " - paper [1004/1898]: On the Convergence of Smooth Regularized Approximate Value Iteration Schemes\n", 583 | " - paper [1006/1898]: Off-Policy Evaluation via the Regularized Lagrangian\n", 584 | " - paper [1008/1898]: The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning\n", 585 | " - paper [1010/1898]: Neural Power Units\n", 586 | " - paper [1012/1898]: Towards Scalable Bayesian Learning of Causal DAGs\n", 587 | " - paper [1014/1898]: A Dictionary Approach to Domain-Invariant Learning in Deep Networks\n", 588 | " - paper [1016/1898]: Bootstrapping neural processes\n", 589 | " - paper [1018/1898]: Large-Scale Adversarial Training for Vision-and-Language Representation Learning\n", 590 | " - paper [1020/1898]: Most ReLU Networks Suffer from $\\ell^2$ Adversarial Perturbations\n", 591 | " - paper [1022/1898]: Compositional Visual Generation with Energy Based Models\n", 592 | " - paper [1024/1898]: Factor Graph Grammars\n", 593 | " - paper [1026/1898]: Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs\n", 594 | " - paper [1028/1898]: Autoregressive Score Matching\n" 595 | ] 596 | }, 597 | { 598 | "name": "stdout", 599 | "output_type": "stream", 600 | "text": [ 601 | " - paper [1030/1898]: Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization\n", 602 | " - paper [1032/1898]: Neural Controlled Differential Equations for Irregular Time Series\n", 603 | " - paper [1034/1898]: On Efficiency in Hierarchical Reinforcement Learning\n", 604 | " - paper [1036/1898]: On Correctness of Automatic Differentiation for Non-Differentiable Functions\n", 605 | " - paper [1038/1898]: Probabilistic Linear Solvers for Machine Learning\n", 606 | " - paper [1040/1898]: Dynamic Regret of Policy Optimization in Non-Stationary Environments\n", 607 | " - paper [1042/1898]: Multipole Graph Neural Operator for Parametric Partial Differential Equations\n", 608 | " - paper [1044/1898]: BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images\n", 609 | " - paper [1046/1898]: Online Structured Meta-learning\n", 610 | " - paper [1048/1898]: Learning Strategic Network Emergence Games\n", 611 | " - paper [1050/1898]: Towards Interpretable Natural Language Understanding with Explanations as Latent Variables\n", 612 | " - paper [1052/1898]: The Mean-Squared Error of Double Q-Learning\n", 613 | " - paper [1054/1898]: What Makes for Good Views for Contrastive Learning?\n", 614 | " - paper [1056/1898]: Denoising Diffusion Probabilistic Models\n", 615 | " - paper [1058/1898]: Barking up the right tree: an approach to search over molecule synthesis DAGs\n", 616 | " - paper [1060/1898]: On Uniform Convergence and Low-Norm Interpolation Learning\n", 617 | " - paper [1062/1898]: Bandit Samplers for Training Graph Neural Networks\n", 618 | " - paper [1064/1898]: Sampling from a k-DPP without looking at all items\n", 619 | " - paper [1066/1898]: Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence\n", 620 | " - paper [1068/1898]: Hierarchical Poset Decoding for Compositional Generalization in Language\n", 621 | " - paper [1070/1898]: Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions\n", 622 | " - paper [1072/1898]: Exchangeable Neural ODE for Set Modeling\n", 623 | " - paper [1074/1898]: Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions\n", 624 | " - paper [1076/1898]: CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection\n", 625 | " - paper [1078/1898]: Regularized linear autoencoders recover the principal components, eventually\n", 626 | " - paper [1080/1898]: Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization\n", 627 | " - paper [1082/1898]: GramGAN: Deep 3D Texture Synthesis From 2D Exemplars\n", 628 | " - paper [1084/1898]: UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection\n", 629 | " - paper [1086/1898]: Learning Restricted Boltzmann Machines with Sparse Latent Variables\n", 630 | " - paper [1088/1898]: Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction\n", 631 | " - paper [1090/1898]: Curriculum learning for multilevel budgeted combinatorial problems\n", 632 | " - paper [1092/1898]: FedSplit: an algorithmic framework for fast federated optimization\n", 633 | " - paper [1094/1898]: Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data\n", 634 | " - paper [1096/1898]: Correlation Robust Influence Maximization\n", 635 | " - paper [1098/1898]: Neuronal Gaussian Process Regression\n", 636 | " - paper [1100/1898]: Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model\n", 637 | " - paper [1102/1898]: Synthetic Data Generators -- Sequential and Private\n", 638 | " - paper [1104/1898]: Uncertainty Quantification for Inferring Hawkes Networks\n", 639 | " - paper [1106/1898]: Implicit Distributional Reinforcement Learning\n", 640 | " - paper [1108/1898]: Auxiliary Task Reweighting for Minimum-data Learning\n", 641 | " - paper [1110/1898]: Small Nash Equilibrium Certificates in Very Large Games\n", 642 | " - paper [1112/1898]: Training Linear Finite-State Machines\n", 643 | " - paper [1114/1898]: Efficient active learning of sparse halfspaces with arbitrary bounded noise\n", 644 | " - paper [1116/1898]: Swapping Autoencoder for Deep Image Manipulation\n", 645 | " - paper [1118/1898]: Self-Supervised Few-Shot Learning on Point Clouds\n", 646 | " - paper [1120/1898]: Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC\n", 647 | " - paper [1122/1898]: Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE\n", 648 | " - paper [1124/1898]: RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning\n", 649 | " - paper [1126/1898]: Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning\n", 650 | " - paper [1128/1898]: Interior Point Solving for LP-based prediction+optimisation\n", 651 | " - paper [1130/1898]: A simple normative network approximates local non-Hebbian learning in the cortex\n", 652 | " - paper [1132/1898]: Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks\n", 653 | " - paper [1134/1898]: Understanding the Role of Training Regimes in Continual Learning\n", 654 | " - paper [1136/1898]: Fair regression with Wasserstein barycenters\n", 655 | " - paper [1138/1898]: Training Stronger Baselines for Learning to Optimize\n", 656 | " - paper [1140/1898]: Exactly Computing the Local Lipschitz Constant of ReLU Networks\n", 657 | " - paper [1142/1898]: Strictly Batch Imitation Learning by Energy-based Distribution Matching\n", 658 | " - paper [1144/1898]: On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method\n", 659 | " - paper [1146/1898]: A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems\n", 660 | " - paper [1148/1898]: Generating Correct Answers for Progressive Matrices Intelligence Tests\n", 661 | " - paper [1150/1898]: HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss\n", 662 | " - paper [1152/1898]: Preference learning along multiple criteria: A game-theoretic perspective\n", 663 | " - paper [1154/1898]: Multi-Plane Program Induction with 3D Box Priors\n", 664 | " - paper [1156/1898]: Online Neural Connectivity Estimation with Noisy Group Testing\n", 665 | " - paper [1158/1898]: Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free\n", 666 | " - paper [1160/1898]: Implicit Neural Representations with Periodic Activation Functions\n", 667 | " - paper [1162/1898]: Rotated Binary Neural Network\n", 668 | " - paper [1164/1898]: Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian\n", 669 | " - paper [1166/1898]: Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness\n", 670 | " - paper [1168/1898]: Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment\n", 671 | " - paper [1170/1898]: Hierarchical nucleation in deep neural networks\n", 672 | " - paper [1172/1898]: Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains\n", 673 | " - paper [1174/1898]: Graph Geometry Interaction Learning\n", 674 | " - paper [1176/1898]: Differentiable Augmentation for Data-Efficient GAN Training\n", 675 | " - paper [1178/1898]: Heuristic Domain Adaptation\n", 676 | " - paper [1180/1898]: Learning Certified Individually Fair Representations\n", 677 | " - paper [1182/1898]: Part-dependent Label Noise: Towards Instance-dependent Label Noise\n", 678 | " - paper [1184/1898]: Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization\n", 679 | " - paper [1186/1898]: An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods\n", 680 | " - paper [1188/1898]: Geometric Exploration for Online Control\n", 681 | " - paper [1190/1898]: Automatic Curriculum Learning through Value Disagreement\n", 682 | " - paper [1192/1898]: MRI Banding Removal via Adversarial Training\n", 683 | " - paper [1194/1898]: The NetHack Learning Environment\n", 684 | " - paper [1196/1898]: Language and Visual Entity Relationship Graph for Agent Navigation\n", 685 | " - paper [1198/1898]: ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping\n", 686 | " - paper [1200/1898]: Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks\n", 687 | " - paper [1202/1898]: No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium\n", 688 | " - paper [1204/1898]: Estimating weighted areas under the ROC curve\n", 689 | " - paper [1206/1898]: Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study\n", 690 | " - paper [1208/1898]: Generalized Hindsight for Reinforcement Learning\n", 691 | " - paper [1210/1898]: Critic Regularized Regression\n", 692 | " - paper [1212/1898]: Boosting Adversarial Training with Hypersphere Embedding\n", 693 | " - paper [1214/1898]: Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs\n" 694 | ] 695 | }, 696 | { 697 | "name": "stdout", 698 | "output_type": "stream", 699 | "text": [ 700 | " - paper [1216/1898]: Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows\n", 701 | " - paper [1218/1898]: Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent\n", 702 | " - paper [1220/1898]: Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification\n", 703 | " - paper [1222/1898]: Detecting Hands and Recognizing Physical Contact in the Wild\n", 704 | " - paper [1224/1898]: On the Theory of Transfer Learning: The Importance of Task Diversity\n", 705 | " - paper [1226/1898]: Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards\n", 706 | " - paper [1228/1898]: Neural Star Domain as Primitive Representation\n", 707 | " - paper [1230/1898]: Off-Policy Interval Estimation with Lipschitz Value Iteration\n", 708 | " - paper [1232/1898]: Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics\n", 709 | " - paper [1234/1898]: Deep Statistical Solvers\n", 710 | " - paper [1236/1898]: Distributionally Robust Parametric Maximum Likelihood Estimation\n", 711 | " - paper [1238/1898]: Secretary and Online Matching Problems with Machine Learned Advice\n", 712 | " - paper [1240/1898]: Deep Transformation-Invariant Clustering\n", 713 | " - paper [1242/1898]: Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree\n", 714 | " - paper [1244/1898]: Improving Generalization in Reinforcement Learning with Mixture Regularization\n", 715 | " - paper [1246/1898]: Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework\n", 716 | " - paper [1248/1898]: Learning from Aggregate Observations\n", 717 | " - paper [1250/1898]: The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models\n", 718 | " - paper [1252/1898]: Subgraph Neural Networks\n", 719 | " - paper [1254/1898]: Demystifying Orthogonal Monte Carlo and Beyond\n", 720 | " - paper [1256/1898]: Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms\n", 721 | " - paper [1258/1898]: A Scalable Approach for Privacy-Preserving Collaborative Machine Learning\n", 722 | " - paper [1260/1898]: Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search\n", 723 | " - paper [1262/1898]: Towards Learning Convolutions from Scratch\n", 724 | " - paper [1264/1898]: Cycle-Contrast for Self-Supervised Video Representation Learning\n", 725 | " - paper [1266/1898]: Posterior Re-calibration for Imbalanced Datasets\n", 726 | " - paper [1268/1898]: Novelty Search in Representational Space for Sample Efficient Exploration\n", 727 | " - paper [1270/1898]: Robust Reinforcement Learning via Adversarial training with Langevin Dynamics\n", 728 | " - paper [1272/1898]: Adversarial Blocking Bandits\n", 729 | " - paper [1274/1898]: Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice\n", 730 | " - paper [1276/1898]: Multi-label Contrastive Predictive Coding\n", 731 | " - paper [1278/1898]: Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud\n", 732 | " - paper [1280/1898]: Learning Invariants through Soft Unification\n", 733 | " - paper [1282/1898]: One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL\n", 734 | " - paper [1284/1898]: Variational Bayesian Monte Carlo with Noisy Likelihoods\n", 735 | " - paper [1286/1898]: Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes\n", 736 | " - paper [1288/1898]: Self-Supervised Generative Adversarial Compression\n", 737 | " - paper [1290/1898]: An efficient nonconvex reformulation of stagewise convex optimization problems\n", 738 | " - paper [1292/1898]: From Finite to Countable-Armed Bandits\n", 739 | " - paper [1294/1898]: Adversarial Distributional Training for Robust Deep Learning\n", 740 | " - paper [1296/1898]: Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes\n", 741 | " - paper [1298/1898]: Theory-Inspired Path-Regularized Differential Network Architecture Search\n", 742 | " - paper [1300/1898]: Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices\n", 743 | " - paper [1302/1898]: Learning the Geometry of Wave-Based Imaging\n", 744 | " - paper [1304/1898]: Greedy inference with structure-exploiting lazy maps\n", 745 | " - paper [1306/1898]: Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning\n", 746 | " - paper [1308/1898]: Finding the Homology of Decision Boundaries with Active Learning\n", 747 | " - paper [1310/1898]: Reinforced Molecular Optimization with Neighborhood-Controlled Grammars\n", 748 | " - paper [1312/1898]: Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes\n", 749 | " - paper [1314/1898]: Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability\n", 750 | " - paper [1316/1898]: Certified Defense to Image Transformations via Randomized Smoothing\n", 751 | " - paper [1318/1898]: Estimation of Skill Distribution from a Tournament\n", 752 | " - paper [1320/1898]: Reparameterizing Mirror Descent as Gradient Descent\n", 753 | " - paper [1322/1898]: General Control Functions for Causal Effect Estimation from IVs\n", 754 | " - paper [1324/1898]: Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards\n", 755 | " - paper [1326/1898]: Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks\n", 756 | " - paper [1328/1898]: Zero-Resource Knowledge-Grounded Dialogue Generation\n", 757 | " - paper [1330/1898]: Targeted Adversarial Perturbations for Monocular Depth Prediction\n", 758 | " - paper [1332/1898]: Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties\n", 759 | " - paper [1334/1898]: Offline Imitation Learning with a Misspecified Simulator\n", 760 | " - paper [1336/1898]: Multi-Fidelity Bayesian Optimization via Deep Neural Networks\n", 761 | " - paper [1338/1898]: PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals\n", 762 | " - paper [1340/1898]: Bad Global Minima Exist and SGD Can Reach Them\n", 763 | " - paper [1342/1898]: Optimal Prediction of the Number of Unseen Species with Multiplicity\n", 764 | " - paper [1344/1898]: Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe\n", 765 | " - paper [1346/1898]: Factor Graph Neural Networks\n", 766 | " - paper [1348/1898]: A Closer Look at Accuracy vs. Robustness\n", 767 | " - paper [1350/1898]: Curriculum Learning by Dynamic Instance Hardness\n", 768 | " - paper [1352/1898]: Spin-Weighted Spherical CNNs\n", 769 | " - paper [1354/1898]: Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks\n", 770 | " - paper [1356/1898]: AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference\n", 771 | " - paper [1358/1898]: Baxter Permutation Process\n", 772 | " - paper [1360/1898]: Characterizing emergent representations in a space of candidate learning rules for deep networks\n", 773 | " - paper [1362/1898]: Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation\n", 774 | " - paper [1364/1898]: Adaptive Probing Policies for Shortest Path Routing\n", 775 | " - paper [1366/1898]: Approximate Heavily-Constrained Learning with Lagrange Multiplier Models\n", 776 | " - paper [1368/1898]: Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs\n", 777 | " - paper [1370/1898]: Sliding Window Algorithms for k-Clustering Problems\n", 778 | " - paper [1372/1898]: AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning\n", 779 | " - paper [1374/1898]: Approximate Cross-Validation for Structured Models\n", 780 | " - paper [1376/1898]: Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation\n", 781 | " - paper [1378/1898]: Debiased Contrastive Learning\n", 782 | " - paper [1380/1898]: UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree\n", 783 | " - paper [1382/1898]: Generalized Boosting\n", 784 | " - paper [1384/1898]: COT-GAN: Generating Sequential Data via Causal Optimal Transport\n", 785 | " - paper [1386/1898]: Impossibility Results for Grammar-Compressed Linear Algebra\n", 786 | " - paper [1388/1898]: Understanding spiking networks through convex optimization\n", 787 | " - paper [1390/1898]: Better Full-Matrix Regret via Parameter-Free Online Learning\n", 788 | " - paper [1392/1898]: Large-Scale Methods for Distributionally Robust Optimization\n", 789 | " - paper [1394/1898]: Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring\n", 790 | " - paper [1396/1898]: Bandit Linear Control\n", 791 | " - paper [1398/1898]: Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals\n", 792 | " - paper [1400/1898]: PEP: Parameter Ensembling by Perturbation\n", 793 | " - paper [1402/1898]: Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View\n" 794 | ] 795 | }, 796 | { 797 | "name": "stdout", 798 | "output_type": "stream", 799 | "text": [ 800 | " - paper [1404/1898]: Adversarial Example Games\n", 801 | " - paper [1406/1898]: Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts\n", 802 | " - paper [1408/1898]: Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach\n", 803 | " - paper [1410/1898]: Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms\n", 804 | " - paper [1412/1898]: Learning to Play Sequential Games versus Unknown Opponents\n", 805 | " - paper [1414/1898]: Further Analysis of Outlier Detection with Deep Generative Models\n", 806 | " - paper [1416/1898]: Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning\n", 807 | " - paper [1418/1898]: Neural Networks Learning and Memorization with (almost) no Over-Parameterization\n", 808 | " - paper [1420/1898]: Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits\n", 809 | " - paper [1422/1898]: Towards a Combinatorial Characterization of Bounded-Memory Learning\n", 810 | " - paper [1424/1898]: Chaos, Extremism and Optimism: Volume Analysis of Learning in Games\n", 811 | " - paper [1426/1898]: On Regret with Multiple Best Arms\n", 812 | " - paper [1428/1898]: Matrix Completion with Hierarchical Graph Side Information\n", 813 | " - paper [1430/1898]: Is Long Horizon RL More Difficult Than Short Horizon RL?\n", 814 | " - paper [1432/1898]: Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond\n", 815 | " - paper [1434/1898]: Adversarial Learning for Robust Deep Clustering\n", 816 | " - paper [1436/1898]: Learning Mutational Semantics\n", 817 | " - paper [1438/1898]: Learning to Learn Variational Semantic Memory\n", 818 | " - paper [1440/1898]: Myersonian Regression\n", 819 | " - paper [1442/1898]: Learnability with Indirect Supervision Signals\n", 820 | " - paper [1444/1898]: Towards Safe Policy Improvement for Non-Stationary MDPs\n", 821 | " - paper [1446/1898]: Finer Metagenomic Reconstruction via Biodiversity Optimization\n", 822 | " - paper [1448/1898]: Causal Discovery in Physical Systems from Videos\n", 823 | " - paper [1450/1898]: Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data\n", 824 | " - paper [1452/1898]: Smoothed Analysis of Online and Differentially Private Learning\n", 825 | " - paper [1454/1898]: Self-Paced Deep Reinforcement Learning\n", 826 | " - paper [1456/1898]: Kalman Filtering Attention for User Behavior Modeling in CTR Prediction\n", 827 | " - paper [1458/1898]: Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples\n", 828 | " - paper [1460/1898]: Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels\n", 829 | " - paper [1462/1898]: GNNGuard: Defending Graph Neural Networks against Adversarial Attacks\n", 830 | " - paper [1464/1898]: Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction\n", 831 | " - paper [1466/1898]: Optimal visual search based on a model of target detectability in natural images\n", 832 | " - paper [1468/1898]: Towards Convergence Rate Analysis of Random Forests for Classification\n", 833 | " - paper [1470/1898]: List-Decodable Mean Estimation via Iterative Multi-Filtering\n", 834 | " - paper [1472/1898]: Exact Recovery of Mangled Clusters with Same-Cluster Queries\n", 835 | " - paper [1474/1898]: Steady State Analysis of Episodic Reinforcement Learning\n", 836 | " - paper [1476/1898]: Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures\n", 837 | " - paper [1478/1898]: Bayesian Optimization for Iterative Learning\n", 838 | " - paper [1480/1898]: Minimax Bounds for Generalized Linear Models\n", 839 | " - paper [1482/1898]: Projection Robust Wasserstein Distance and Riemannian Optimization\n", 840 | " - paper [1484/1898]: CoinDICE: Off-Policy Confidence Interval Estimation\n", 841 | " - paper [1486/1898]: Simple and Fast Algorithm for Binary Integer and Online Linear Programming\n", 842 | " - paper [1488/1898]: Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction\n", 843 | " - paper [1490/1898]: Learning Rich Rankings\n", 844 | " - paper [1492/1898]: Color Visual Illusions: A Statistics-based Computational Model\n", 845 | " - paper [1494/1898]: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\n", 846 | " - paper [1496/1898]: Universal guarantees for decision tree induction via a higher-order splitting criterion\n", 847 | " - paper [1498/1898]: Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation\n", 848 | " - paper [1500/1898]: A Boolean Task Algebra for Reinforcement Learning\n", 849 | " - paper [1502/1898]: Learning with Differentiable Pertubed Optimizers\n", 850 | " - paper [1504/1898]: Optimal Learning from Verified Training Data\n", 851 | " - paper [1506/1898]: Online Linear Optimization with Many Hints\n", 852 | " - paper [1508/1898]: Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification\n", 853 | " - paper [1510/1898]: Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning\n", 854 | " - paper [1512/1898]: Exploiting the Surrogate Gap in Online Multiclass Classification\n", 855 | " - paper [1514/1898]: The Pitfalls of Simplicity Bias in Neural Networks\n", 856 | " - paper [1516/1898]: Automatically Learning Compact Quality-aware Surrogates for Optimization Problems\n", 857 | " - paper [1518/1898]: Empirical Likelihood for Contextual Bandits\n", 858 | " - paper [1520/1898]: Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?\n", 859 | " - paper [1522/1898]: Non-reversible Gaussian processes for identifying latent dynamical structure in neural data\n", 860 | " - paper [1524/1898]: Listening to Sounds of Silence for Speech Denoising\n", 861 | " - paper [1526/1898]: BoxE: A Box Embedding Model for Knowledge Base Completion\n", 862 | " - paper [1528/1898]: Coherent Hierarchical Multi-Label Classification Networks\n", 863 | " - paper [1530/1898]: Walsh-Hadamard Variational Inference for Bayesian Deep Learning\n", 864 | " - paper [1532/1898]: Federated Bayesian Optimization via Thompson Sampling\n", 865 | " - paper [1534/1898]: MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation\n", 866 | " - paper [1536/1898]: Neural Complexity Measures\n", 867 | " - paper [1538/1898]: Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform\n", 868 | " - paper [1540/1898]: Provably adaptive reinforcement learning in metric spaces\n", 869 | " - paper [1542/1898]: ShapeFlow: Learnable Deformation Flows Among 3D Shapes\n", 870 | " - paper [1544/1898]: Self-Supervised Learning by Cross-Modal Audio-Video Clustering\n", 871 | " - paper [1546/1898]: Optimal Query Complexity of Secure Stochastic Convex Optimization\n", 872 | " - paper [1548/1898]: DynaBERT: Dynamic BERT with Adaptive Width and Depth\n", 873 | " - paper [1550/1898]: Generalization Bound of Gradient Descent for Non-Convex Metric Learning\n", 874 | " - paper [1552/1898]: Dynamic Submodular Maximization\n", 875 | " - paper [1554/1898]: Inference for Batched Bandits\n", 876 | " - paper [1556/1898]: Approximate Cross-Validation with Low-Rank Data in High Dimensions\n", 877 | " - paper [1558/1898]: GANSpace: Discovering Interpretable GAN Controls\n", 878 | " - paper [1560/1898]: Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization\n", 879 | " - paper [1562/1898]: Neuron-level Structured Pruning using Polarization Regularizer\n", 880 | " - paper [1564/1898]: Limits on Testing Structural Changes in Ising Models\n", 881 | " - paper [1566/1898]: Field-wise Learning for Multi-field Categorical Data\n", 882 | " - paper [1568/1898]: Continual Learning in Low-rank Orthogonal Subspaces\n", 883 | " - paper [1570/1898]: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments\n", 884 | " - paper [1572/1898]: Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms\n", 885 | " - paper [1574/1898]: Learning Deformable Tetrahedral Meshes for 3D Reconstruction\n", 886 | " - paper [1576/1898]: Information theoretic limits of learning a sparse rule\n", 887 | " - paper [1578/1898]: Self-supervised learning through the eyes of a child\n", 888 | " - paper [1580/1898]: Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning\n", 889 | " - paper [1582/1898]: A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning\n", 890 | " - paper [1584/1898]: What shapes feature representations? Exploring datasets, architectures, and training\n", 891 | " - paper [1586/1898]: Optimal Best-arm Identification in Linear Bandits\n", 892 | " - paper [1588/1898]: Data Diversification: A Simple Strategy For Neural Machine Translation\n", 893 | " - paper [1590/1898]: Interstellar: Searching Recurrent Architecture for \tKnowledge Graph Embedding\n" 894 | ] 895 | }, 896 | { 897 | "name": "stdout", 898 | "output_type": "stream", 899 | "text": [ 900 | " - paper [1592/1898]: CoSE: Compositional Stroke Embeddings\n", 901 | " - paper [1594/1898]: Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks\n", 902 | " - paper [1596/1898]: Biological credit assignment through dynamic inversion of feedforward networks\n", 903 | " - paper [1598/1898]: Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching\n", 904 | " - paper [1600/1898]: Learning Multi-Agent Communication through Structured Attentive Reasoning\n", 905 | " - paper [1602/1898]: Private Identity Testing for High-Dimensional Distributions\n", 906 | " - paper [1604/1898]: On the Optimal Weighted $\\ell_2$ Regularization in Overparameterized Linear Regression\n", 907 | " - paper [1606/1898]: An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search\n", 908 | " - paper [1608/1898]: MetaSDF: Meta-Learning Signed Distance Functions\n", 909 | " - paper [1610/1898]: Simple and Scalable Sparse k-means Clustering via Feature Ranking\n", 910 | " - paper [1612/1898]: Model-based Adversarial Meta-Reinforcement Learning\n", 911 | " - paper [1614/1898]: Graph Policy Network for Transferable Active Learning on Graphs\n", 912 | " - paper [1616/1898]: Towards a Better Global Loss Landscape of GANs\n", 913 | " - paper [1618/1898]: Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning\n", 914 | " - paper [1620/1898]: BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits\n", 915 | " - paper [1622/1898]: UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging\n", 916 | " - paper [1624/1898]: Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders\n", 917 | " - paper [1626/1898]: An Unbiased Risk Estimator for Learning with Augmented Classes\n", 918 | " - paper [1628/1898]: AutoBSS: An Efficient Algorithm for Block Stacking Style Search\n", 919 | " - paper [1630/1898]: Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point\n", 920 | " - paper [1632/1898]: Stochastic Optimization with Laggard Data Pipelines\n", 921 | " - paper [1634/1898]: Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs\n", 922 | " - paper [1636/1898]: GPS-Net: Graph-based Photometric Stereo Network\n", 923 | " - paper [1638/1898]: Consistent Structural Relation Learning for Zero-Shot Segmentation\n", 924 | " - paper [1640/1898]: Model Selection in Contextual Stochastic Bandit Problems\n", 925 | " - paper [1642/1898]: Truncated Linear Regression in High Dimensions\n", 926 | " - paper [1644/1898]: Incorporating Pragmatic Reasoning Communication into Emergent Language\n", 927 | " - paper [1646/1898]: Deep Subspace Clustering with Data Augmentation\n", 928 | " - paper [1648/1898]: An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits\n", 929 | " - paper [1650/1898]: Can Graph Neural Networks Count Substructures?\n", 930 | " - paper [1652/1898]: A Bayesian Perspective on Training Speed and Model Selection\n", 931 | " - paper [1654/1898]: On the Modularity of Hypernetworks\n", 932 | " - paper [1656/1898]: Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies\n", 933 | " - paper [1658/1898]: Provably Efficient Neural GTD for Off-Policy Learning\n", 934 | " - paper [1660/1898]: Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration\n", 935 | " - paper [1662/1898]: Stable and expressive recurrent vision models\n", 936 | " - paper [1664/1898]: Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form\n", 937 | " - paper [1666/1898]: BRP-NAS: Prediction-based NAS using GCNs\n", 938 | " - paper [1668/1898]: Deep Shells: Unsupervised Shape Correspondence with Optimal Transport\n", 939 | " - paper [1670/1898]: ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding\n", 940 | " - paper [1672/1898]: Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D\n", 941 | " - paper [1674/1898]: Regularizing Black-box Models for Improved Interpretability\n", 942 | " - paper [1676/1898]: Trust the Model When It Is Confident: Masked Model-based Actor-Critic\n", 943 | " - paper [1678/1898]: Semi-Supervised Neural Architecture Search\n", 944 | " - paper [1680/1898]: Consistency Regularization for Certified Robustness of Smoothed Classifiers\n", 945 | " - paper [1682/1898]: Robust Multi-Agent Reinforcement Learning with Model Uncertainty\n", 946 | " - paper [1684/1898]: SIRI: Spatial Relation Induced Network For Spatial Description Resolution\n", 947 | " - paper [1686/1898]: Adaptive Shrinkage Estimation for Streaming Graphs\n", 948 | " - paper [1688/1898]: Make One-Shot Video Object Segmentation Efficient Again\n", 949 | " - paper [1690/1898]: Depth Uncertainty in Neural Networks\n", 950 | " - paper [1692/1898]: Non-Euclidean Universal Approximation\n", 951 | " - paper [1694/1898]: Constraining Variational Inference with Geometric Jensen-Shannon Divergence\n", 952 | " - paper [1696/1898]: Gibbs Sampling with People\n", 953 | " - paper [1698/1898]: HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory\n", 954 | " - paper [1700/1898]: FrugalML: How to use ML Prediction APIs more accurately and cheaply\n", 955 | " - paper [1702/1898]: Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth\n", 956 | " - paper [1704/1898]: Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning\n", 957 | " - paper [1706/1898]: Monotone operator equilibrium networks\n", 958 | " - paper [1708/1898]: When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes\n", 959 | " - paper [1710/1898]: Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control\n", 960 | " - paper [1712/1898]: High-Dimensional Sparse Linear Bandits\n", 961 | " - paper [1714/1898]: Non-Stochastic Control with Bandit Feedback\n", 962 | " - paper [1716/1898]: Generalized Leverage Score Sampling for Neural Networks\n", 963 | " - paper [1718/1898]: An Optimal Elimination Algorithm for Learning a Best Arm\n", 964 | " - paper [1720/1898]: Efficient Projection-free Algorithms for Saddle Point Problems\n", 965 | " - paper [1722/1898]: A mathematical model for automatic differentiation in machine learning\n", 966 | " - paper [1724/1898]: Unsupervised Text Generation by Learning from Search\n", 967 | " - paper [1726/1898]: Learning Compositional Rules via Neural Program Synthesis\n", 968 | " - paper [1728/1898]: Incorporating BERT into Parallel Sequence Decoding with Adapters\n", 969 | " - paper [1730/1898]: Estimating Fluctuations in Neural Representations of Uncertain Environments\n", 970 | " - paper [1732/1898]: Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation\n", 971 | " - paper [1734/1898]: SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm\n", 972 | " - paper [1736/1898]: Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks\n", 973 | " - paper [1738/1898]: General Transportability of Soft Interventions: Completeness Results\n", 974 | " - paper [1740/1898]: GAIT-prop: A biologically plausible learning rule derived from backpropagation of error\n", 975 | " - paper [1742/1898]: Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing\n", 976 | " - paper [1744/1898]: SCOP: Scientific Control for Reliable Neural Network Pruning\n", 977 | " - paper [1746/1898]: Provably Consistent Partial-Label Learning\n", 978 | " - paper [1748/1898]: Robust, Accurate Stochastic Optimization for Variational Inference\n", 979 | " - paper [1750/1898]: Discovering conflicting groups in signed networks\n", 980 | " - paper [1752/1898]: Learning Some Popular Gaussian Graphical Models without Condition Number Bounds\n", 981 | " - paper [1754/1898]: Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding\n", 982 | " - paper [1756/1898]: Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions\n", 983 | " - paper [1758/1898]: Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition\n", 984 | " - paper [1760/1898]: VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain\n", 985 | " - paper [1762/1898]: The Smoothed Possibility of Social Choice\n", 986 | " - paper [1764/1898]: A Decentralized Parallel Algorithm for Training Generative Adversarial Nets\n", 987 | " - paper [1766/1898]: Phase retrieval in high dimensions: Statistical and computational phase transitions\n", 988 | " - paper [1768/1898]: Fair Performance Metric Elicitation\n", 989 | " - paper [1770/1898]: Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function\n", 990 | " - paper [1772/1898]: Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information\n", 991 | " - paper [1774/1898]: Soft Contrastive Learning for Visual Localization\n" 992 | ] 993 | }, 994 | { 995 | "name": "stdout", 996 | "output_type": "stream", 997 | "text": [ 998 | " - paper [1776/1898]: Fine-Grained Dynamic Head for Object Detection\n", 999 | " - paper [1778/1898]: LoCo: Local Contrastive Representation Learning\n", 1000 | " - paper [1780/1898]: Modeling and Optimization Trade-off in Meta-learning\n", 1001 | " - paper [1782/1898]: SnapBoost: A Heterogeneous Boosting Machine\n", 1002 | " - paper [1784/1898]: On Adaptive Distance Estimation\n", 1003 | " - paper [1786/1898]: Stage-wise Conservative Linear Bandits\n", 1004 | " - paper [1788/1898]: RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces\n", 1005 | " - paper [1790/1898]: Metric-Free Individual Fairness in Online Learning\n", 1006 | " - paper [1792/1898]: GreedyFool: Distortion-Aware Sparse Adversarial Attack\n", 1007 | " - paper [1794/1898]: VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data\n", 1008 | " - paper [1796/1898]: RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist\n", 1009 | " - paper [1798/1898]: Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining\n", 1010 | " - paper [1800/1898]: Improved Sample Complexity for Incremental Autonomous Exploration in MDPs\n", 1011 | " - paper [1802/1898]: TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning\n", 1012 | " - paper [1804/1898]: RD$^2$: Reward Decomposition with Representation Decomposition\n", 1013 | " - paper [1806/1898]: Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID\n", 1014 | " - paper [1808/1898]: Fairness constraints can help exact inference in structured prediction\n", 1015 | " - paper [1810/1898]: Instance-based Generalization in Reinforcement Learning\n", 1016 | " - paper [1812/1898]: Smooth And Consistent Probabilistic Regression Trees\n", 1017 | " - paper [1814/1898]: Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming\n", 1018 | " - paper [1816/1898]: Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses\n", 1019 | " - paper [1818/1898]: Winning the Lottery with Continuous Sparsification\n", 1020 | " - paper [1820/1898]: Adversarial robustness via robust low rank representations\n", 1021 | " - paper [1822/1898]: Joints in Random Forests\n", 1022 | " - paper [1824/1898]: Compositional Generalization by Learning Analytical Expressions\n", 1023 | " - paper [1826/1898]: JAX MD: A Framework for Differentiable Physics\n", 1024 | " - paper [1828/1898]: An implicit function learning approach for parametric modal regression\n", 1025 | " - paper [1830/1898]: SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images\n", 1026 | " - paper [1832/1898]: Coresets for Robust Training of Deep Neural Networks against Noisy Labels\n", 1027 | " - paper [1834/1898]: Adapting to Misspecification in Contextual Bandits\n", 1028 | " - paper [1836/1898]: Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters\n", 1029 | " - paper [1838/1898]: MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures\n", 1030 | " - paper [1840/1898]: Learning to solve TV regularised problems with unrolled algorithms\n", 1031 | " - paper [1842/1898]: Object-Centric Learning with Slot Attention\n", 1032 | " - paper [1844/1898]: Improving robustness against common corruptions by covariate shift adaptation\n", 1033 | " - paper [1846/1898]: Deep Smoothing of the Implied Volatility Surface\n", 1034 | " - paper [1848/1898]: Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations\n", 1035 | " - paper [1850/1898]: Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning\n", 1036 | " - paper [1852/1898]: Look-ahead Meta Learning for Continual Learning\n", 1037 | " - paper [1854/1898]: A polynomial-time algorithm for learning nonparametric causal graphs\n", 1038 | " - paper [1856/1898]: Sparse Learning with CART\n", 1039 | " - paper [1858/1898]: Proximal Mapping for Deep Regularization\n", 1040 | " - paper [1860/1898]: Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models\n", 1041 | " - paper [1862/1898]: Hierarchical Granularity Transfer Learning\n", 1042 | " - paper [1864/1898]: Deep active inference agents using Monte-Carlo methods\n", 1043 | " - paper [1866/1898]: Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations\n", 1044 | " - paper [1868/1898]: Manifold structure in graph embeddings\n", 1045 | " - paper [1870/1898]: Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web\n", 1046 | " - paper [1872/1898]: MCUNet: Tiny Deep Learning on IoT Devices\n", 1047 | " - paper [1874/1898]: In search of robust measures of generalization\n", 1048 | " - paper [1876/1898]: Task-agnostic Exploration in Reinforcement Learning\n", 1049 | " - paper [1878/1898]: Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery\n", 1050 | " - paper [1880/1898]: Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration\n", 1051 | " - paper [1882/1898]: Softmax Deep Double Deterministic Policy Gradients\n", 1052 | " - paper [1884/1898]: Online Decision Based Visual Tracking via Reinforcement Learning\n", 1053 | " - paper [1886/1898]: Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity\n", 1054 | " - paper [1888/1898]: DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs\n", 1055 | " - paper [1890/1898]: Distributional Robustness with IPMs and links to Regularization and GANs\n", 1056 | " - paper [1892/1898]: A shooting formulation of deep learning\n", 1057 | " - paper [1894/1898]: CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances\n", 1058 | " - paper [1896/1898]: Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning\n", 1059 | " - paper [1898/1898]: MATE: Plugging in Model Awareness to Task Embedding for Meta Learning\n", 1060 | " - paper [1900/1898]: Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits\n", 1061 | " - paper [1902/1898]: Predictive Information Accelerates Learning in RL\n", 1062 | " - paper [1904/1898]: Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization\n", 1063 | " - paper [1906/1898]: High-Fidelity Generative Image Compression\n", 1064 | " - paper [1908/1898]: A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning\n", 1065 | " - paper [1910/1898]: Counterexample-Guided Learning of Monotonic Neural Networks\n", 1066 | " - paper [1912/1898]: A Novel Approach for Constrained Optimization in Graphical Models\n", 1067 | " - paper [1914/1898]: Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology\n", 1068 | " - paper [1916/1898]: On the Trade-off between Adversarial and Backdoor Robustness\n", 1069 | " - paper [1918/1898]: Implicit Graph Neural Networks\n", 1070 | " - paper [1920/1898]: Rethinking Importance Weighting for Deep Learning under Distribution Shift\n", 1071 | " - paper [1922/1898]: Guiding Deep Molecular Optimization with Genetic Exploration\n", 1072 | " - paper [1924/1898]: Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks\n", 1073 | " - paper [1926/1898]: TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation\n", 1074 | " - paper [1928/1898]: Neural Topographic Factor Analysis for fMRI Data\n", 1075 | " - paper [1930/1898]: Neural Architecture Generator Optimization\n", 1076 | " - paper [1932/1898]: A Bandit Learning Algorithm and Applications to Auction Design\n", 1077 | " - paper [1934/1898]: MetaPoison: Practical General-purpose Clean-label Data Poisoning\n", 1078 | " - paper [1936/1898]: Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation\n", 1079 | " - paper [1938/1898]: Training Generative Adversarial Networks with Limited Data\n", 1080 | " - paper [1940/1898]: Deeply Learned Spectral Total Variation Decomposition\n", 1081 | " - paper [1942/1898]: FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training\n", 1082 | " - paper [1944/1898]: Improving Neural Network Training in Low Dimensional Random Bases\n", 1083 | " - paper [1946/1898]: Safe Reinforcement Learning via Curriculum Induction\n", 1084 | " - paper [1948/1898]: Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning\n", 1085 | " - paper [1950/1898]: How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?\n", 1086 | " - paper [1952/1898]: Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses\n", 1087 | " - paper [1954/1898]: Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization\n", 1088 | " - paper [1956/1898]: Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method\n", 1089 | " - paper [1958/1898]: PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks\n", 1090 | " - paper [1960/1898]: Few-Cost Salient Object Detection with Adversarial-Paced Learning\n" 1091 | ] 1092 | }, 1093 | { 1094 | "name": "stdout", 1095 | "output_type": "stream", 1096 | "text": [ 1097 | " - paper [1962/1898]: Minimax Estimation of Conditional Moment Models\n", 1098 | " - paper [1964/1898]: Causal Imitation Learning With Unobserved Confounders\n", 1099 | " - paper [1966/1898]: Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling\n", 1100 | " - paper [1968/1898]: Learning Black-Box Attackers with Transferable Priors and Query Feedback\n", 1101 | " - paper [1970/1898]: Locally Differentially Private (Contextual) Bandits Learning\n", 1102 | " - paper [1972/1898]: Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax\n", 1103 | " - paper [1974/1898]: Kernel Based Progressive Distillation for Adder Neural Networks\n", 1104 | " - paper [1976/1898]: Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization\n", 1105 | " - paper [1978/1898]: Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space\n", 1106 | " - paper [1980/1898]: The Wasserstein Proximal Gradient Algorithm\n", 1107 | " - paper [1982/1898]: Universally Quantized Neural Compression\n", 1108 | " - paper [1984/1898]: Temporal Variability in Implicit Online Learning\n", 1109 | " - paper [1986/1898]: Investigating Gender Bias in Language Models Using Causal Mediation Analysis\n", 1110 | " - paper [1988/1898]: Off-Policy Imitation Learning from Observations\n", 1111 | " - paper [1990/1898]: Escaping Saddle-Point Faster under Interpolation-like Conditions\n", 1112 | " - paper [1992/1898]: Matérn Gaussian Processes on Riemannian Manifolds\n", 1113 | " - paper [1994/1898]: Improved Techniques for Training Score-Based Generative Models\n", 1114 | " - paper [1996/1898]: wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations\n", 1115 | " - paper [1998/1898]: A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs\n", 1116 | " - paper [2000/1898]: Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients\n", 1117 | " - paper [2002/1898]: Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?\n", 1118 | " - paper [2004/1898]: Value-driven Hindsight Modelling\n", 1119 | " - paper [2006/1898]: Dynamic Regret of Convex and Smooth Functions\n", 1120 | " - paper [2008/1898]: On Convergence of Nearest Neighbor Classifiers over Feature Transformations\n", 1121 | " - paper [2010/1898]: Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments\n", 1122 | " - paper [2012/1898]: Contrastive learning of global and local features for medical image segmentation with limited annotations\n", 1123 | " - paper [2014/1898]: Self-Supervised Graph Transformer on Large-Scale Molecular Data\n", 1124 | " - paper [2016/1898]: Generative Neurosymbolic Machines\n", 1125 | " - paper [2018/1898]: How many samples is a good initial point worth in Low-rank Matrix Recovery?\n", 1126 | " - paper [2020/1898]: CSER: Communication-efficient SGD with Error Reset\n", 1127 | " - paper [2022/1898]: Efficient estimation of neural tuning during naturalistic behavior\n", 1128 | " - paper [2024/1898]: High-recall causal discovery for autocorrelated time series with latent confounders\n", 1129 | " - paper [2026/1898]: Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes\n", 1130 | " - paper [2028/1898]: Joint Contrastive Learning with Infinite Possibilities\n", 1131 | " - paper [2030/1898]: Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time\n", 1132 | " - paper [2032/1898]: Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models\n", 1133 | " - paper [2034/1898]: GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators\n", 1134 | " - paper [2036/1898]: SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows\n", 1135 | " - paper [2038/1898]: Learning Causal Effects via Weighted Empirical Risk Minimization\n", 1136 | " - paper [2040/1898]: Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes\n", 1137 | " - paper [2042/1898]: Incorporating Interpretable Output Constraints in Bayesian Neural Networks\n", 1138 | " - paper [2044/1898]: Multi-Stage Influence Function\n", 1139 | " - paper [2046/1898]: Probabilistic Fair Clustering\n", 1140 | " - paper [2048/1898]: Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty\n", 1141 | " - paper [2050/1898]: ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA\n", 1142 | " - paper [2052/1898]: Testing Determinantal Point Processes\n", 1143 | " - paper [2054/1898]: CogLTX: Applying BERT to Long Texts\n", 1144 | " - paper [2056/1898]: f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning\n", 1145 | " - paper [2058/1898]: Non-parametric Models for Non-negative Functions\n", 1146 | " - paper [2060/1898]: Uncertainty Aware Semi-Supervised Learning on Graph Data\n", 1147 | " - paper [2062/1898]: ConvBERT: Improving BERT with Span-based Dynamic Convolution\n", 1148 | " - paper [2064/1898]: Practical No-box Adversarial Attacks against DNNs\n", 1149 | " - paper [2066/1898]: Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model\n", 1150 | " - paper [2068/1898]: Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization\n", 1151 | " - paper [2070/1898]: Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks\n", 1152 | " - paper [2072/1898]: Reward Propagation Using Graph Convolutional Networks\n", 1153 | " - paper [2074/1898]: LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration\n", 1154 | " - paper [2076/1898]: Fully Dynamic Algorithm for Constrained Submodular Optimization\n", 1155 | " - paper [2078/1898]: Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation\n", 1156 | " - paper [2080/1898]: Autofocused oracles for model-based design\n", 1157 | " - paper [2082/1898]: Debiasing Averaged Stochastic Gradient Descent to handle missing values\n", 1158 | " - paper [2084/1898]: Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning\n", 1159 | " - paper [2086/1898]: CompRess: Self-Supervised Learning by Compressing Representations\n", 1160 | " - paper [2088/1898]: Sample complexity and effective dimension for regression on manifolds\n", 1161 | " - paper [2090/1898]: The phase diagram of approximation rates for deep neural networks\n", 1162 | " - paper [2092/1898]: Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network\n", 1163 | " - paper [2094/1898]: EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints\n", 1164 | " - paper [2096/1898]: Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN\n", 1165 | " - paper [2098/1898]: Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design\n", 1166 | " - paper [2100/1898]: A Spectral Energy Distance for Parallel Speech Synthesis\n", 1167 | " - paper [2102/1898]: Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations\n", 1168 | " - paper [2104/1898]: Learning from Positive and Unlabeled Data with Arbitrary Positive Shift\n", 1169 | " - paper [2106/1898]: Deep Energy-based Modeling of Discrete-Time Physics\n", 1170 | " - paper [2108/1898]: Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning\n", 1171 | " - paper [2110/1898]: Self-Learning Transformations for Improving Gaze and Head Redirection\n", 1172 | " - paper [2112/1898]: Language-Conditioned Imitation Learning for Robot Manipulation Tasks\n", 1173 | " - paper [2114/1898]: POMDPs in Continuous Time and Discrete Spaces\n", 1174 | " - paper [2116/1898]: Exemplar Guided Active Learning\n", 1175 | " - paper [2118/1898]: Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps\n", 1176 | " - paper [2120/1898]: Node Embeddings and Exact Low-Rank Representations of Complex Networks\n", 1177 | " - paper [2122/1898]: Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications\n", 1178 | " - paper [2124/1898]: Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction\n", 1179 | " - paper [2126/1898]: On Infinite-Width Hypernetworks\n", 1180 | " - paper [2128/1898]: Interferobot: aligning an optical interferometer by a reinforcement learning agent\n", 1181 | " - paper [2130/1898]: Program Synthesis with Pragmatic Communication\n", 1182 | " - paper [2132/1898]: Principal Neighbourhood Aggregation for Graph Nets\n", 1183 | " - paper [2134/1898]: Reliable Graph Neural Networks via Robust Aggregation\n", 1184 | " - paper [2136/1898]: Instance Selection for GANs\n", 1185 | " - paper [2138/1898]: Linear Disentangled Representations and Unsupervised Action Estimation\n", 1186 | " - paper [2140/1898]: Video Frame Interpolation without Temporal Priors\n", 1187 | " - paper [2142/1898]: Learning compositional functions via multiplicative weight updates\n", 1188 | " - paper [2144/1898]: Sample Complexity of Uniform Convergence for Multicalibration\n" 1189 | ] 1190 | }, 1191 | { 1192 | "name": "stdout", 1193 | "output_type": "stream", 1194 | "text": [ 1195 | " - paper [2146/1898]: Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement\n", 1196 | " - paper [2148/1898]: The interplay between randomness and structure during learning in RNNs\n", 1197 | " - paper [2150/1898]: A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks\n", 1198 | " - paper [2152/1898]: Instance-wise Feature Grouping\n", 1199 | " - paper [2154/1898]: Robust Disentanglement of a Few Factors at a Time\n", 1200 | " - paper [2156/1898]: PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning\n", 1201 | " - paper [2158/1898]: Group Contextual Encoding for 3D Point Clouds\n", 1202 | " - paper [2160/1898]: Latent Bandits Revisited\n", 1203 | " - paper [2162/1898]: Is normalization indispensable for training deep neural network? \n", 1204 | " - paper [2164/1898]: Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions\n", 1205 | " - paper [2166/1898]: Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks\n", 1206 | " - paper [2168/1898]: Linear Time Sinkhorn Divergences using Positive Features\n", 1207 | " - paper [2170/1898]: VarGrad: A Low-Variance Gradient Estimator for Variational Inference\n", 1208 | " - paper [2172/1898]: A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction\n", 1209 | " - paper [2174/1898]: Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method\n", 1210 | " - paper [2176/1898]: Adversarial Counterfactual Learning and Evaluation for Recommender System\n", 1211 | " - paper [2178/1898]: Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control\n", 1212 | " - paper [2180/1898]: Evolving Normalization-Activation Layers\n", 1213 | " - paper [2182/1898]: ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training\n", 1214 | " - paper [2184/1898]: RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder\n", 1215 | " - paper [2186/1898]: Efficient Learning of Discrete Graphical Models\n", 1216 | " - paper [2188/1898]: Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals\n", 1217 | " - paper [2190/1898]: Neurosymbolic Transformers for Multi-Agent Communication\n", 1218 | " - paper [2192/1898]: Fairness in Streaming Submodular Maximization: Algorithms and Hardness\n", 1219 | " - paper [2194/1898]: Smoothed Geometry for Robust Attribution\n", 1220 | " - paper [2196/1898]: Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms\n", 1221 | " - paper [2198/1898]: Multi-agent active perception with prediction rewards\n", 1222 | " - paper [2200/1898]: A Local Temporal Difference Code for Distributional Reinforcement Learning\n", 1223 | " - paper [2202/1898]: Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions\n", 1224 | " - paper [2204/1898]: CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations\n", 1225 | " - paper [2206/1898]: Deep Automodulators\n", 1226 | " - paper [2208/1898]: Convolutional Tensor-Train LSTM for Spatio-Temporal Learning\n", 1227 | " - paper [2210/1898]: The Potts-Ising model for discrete multivariate data\n", 1228 | " - paper [2212/1898]: Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech\n", 1229 | " - paper [2214/1898]: Group-Fair Online Allocation in Continuous Time\n", 1230 | " - paper [2216/1898]: Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis\n", 1231 | " - paper [2218/1898]: Understanding Gradient Clipping in Private SGD: A Geometric Perspective\n", 1232 | " - paper [2220/1898]: O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers\n", 1233 | " - paper [2222/1898]: Identifying signal and noise structure in neural population activity with Gaussian process factor models\n", 1234 | " - paper [2224/1898]: Equivariant Networks for Hierarchical Structures\n", 1235 | " - paper [2226/1898]: MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics\n", 1236 | " - paper [2228/1898]: A Discrete Variational Recurrent Topic Model without the Reparametrization Trick\n", 1237 | " - paper [2230/1898]: Transferable Graph Optimizers for ML Compilers\n", 1238 | " - paper [2232/1898]: Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces\n", 1239 | " - paper [2234/1898]: Learning Bounds for Risk-sensitive Learning\n", 1240 | " - paper [2236/1898]: Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints\n", 1241 | " - paper [2238/1898]: Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency\n", 1242 | " - paper [2240/1898]: Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations\n", 1243 | " - paper [2242/1898]: Constant-Expansion Suffices for Compressed Sensing with Generative Priors\n", 1244 | " - paper [2244/1898]: RANet: Region Attention Network for Semantic Segmentation\n", 1245 | " - paper [2246/1898]: A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent\n", 1246 | " - paper [2248/1898]: Learning sparse codes from compressed representations with biologically plausible local wiring constraints\n", 1247 | " - paper [2250/1898]: Self-Imitation Learning via Generalized Lower Bound Q-learning\n", 1248 | " - paper [2252/1898]: Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity\n", 1249 | " - paper [2254/1898]: Directional Pruning of Deep Neural Networks\n", 1250 | " - paper [2256/1898]: Smoothly Bounding User Contributions in Differential Privacy \n", 1251 | " - paper [2258/1898]: Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping\n", 1252 | " - paper [2260/1898]: Online Planning with Lookahead Policies\n", 1253 | " - paper [2262/1898]: Learning Deep Attribution Priors Based On Prior Knowledge\n", 1254 | " - paper [2264/1898]: Using noise to probe recurrent neural network structure and prune synapses\n", 1255 | " - paper [2266/1898]: NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity\n", 1256 | " - paper [2268/1898]: Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge\n", 1257 | " - paper [2270/1898]: Neural FFTs for Universal Texture Image Synthesis\n", 1258 | " - paper [2272/1898]: Graph Cross Networks with Vertex Infomax Pooling\n", 1259 | " - paper [2274/1898]: Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms\n", 1260 | " - paper [2276/1898]: Calibration of Shared Equilibria in General Sum Partially Observable Markov Games\n", 1261 | " - paper [2278/1898]: MOPO: Model-based Offline Policy Optimization\n", 1262 | " - paper [2280/1898]: Building powerful and equivariant graph neural networks with structural message-passing\n", 1263 | " - paper [2282/1898]: Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning\n", 1264 | " - paper [2284/1898]: Practical Low-Rank Communication Compression in Decentralized Deep Learning\n", 1265 | " - paper [2286/1898]: Mutual exclusivity as a challenge for deep neural networks\n", 1266 | " - paper [2288/1898]: 3D Shape Reconstruction from Vision and Touch\n", 1267 | " - paper [2290/1898]: GradAug: A New Regularization Method for Deep Neural Networks\n", 1268 | " - paper [2292/1898]: An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay\n", 1269 | " - paper [2294/1898]: Learning Utilities and Equilibria in Non-Truthful Auctions\n", 1270 | " - paper [2296/1898]: Rational neural networks\n", 1271 | " - paper [2298/1898]: DISK: Learning local features with policy gradient\n", 1272 | " - paper [2300/1898]: Transfer Learning via $\\ell_1$ Regularization\n", 1273 | " - paper [2302/1898]: GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network\n", 1274 | " - paper [2304/1898]: Deep Inverse Q-learning with Constraints\n", 1275 | " - paper [2306/1898]: Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities\n", 1276 | " - paper [2308/1898]: Prediction with Corrupted Expert Advice\n", 1277 | " - paper [2310/1898]: Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency\n", 1278 | " - paper [2312/1898]: Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition\n", 1279 | " - paper [2314/1898]: Point process models for sequence detection in high-dimensional neural spike trains\n", 1280 | " - paper [2316/1898]: Adversarial Attacks on Linear Contextual Bandits\n", 1281 | " - paper [2318/1898]: Meta-Consolidation for Continual Learning\n", 1282 | " - paper [2320/1898]: Organizing recurrent network dynamics by task-computation to enable continual learning\n", 1283 | " - paper [2322/1898]: Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting\n" 1284 | ] 1285 | }, 1286 | { 1287 | "name": "stdout", 1288 | "output_type": "stream", 1289 | "text": [ 1290 | " - paper [2324/1898]: Kernel Methods Through the Roof: Handling Billions of Points Efficiently\n", 1291 | " - paper [2326/1898]: Spike and slab variational Bayes for high dimensional logistic regression\n", 1292 | " - paper [2328/1898]: Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness\n", 1293 | " - paper [2330/1898]: Fast geometric learning with symbolic matrices\n", 1294 | " - paper [2332/1898]: MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler\n", 1295 | " - paper [2334/1898]: CoinPress: Practical Private Mean and Covariance Estimation\n", 1296 | " - paper [2336/1898]: Planning with General Objective Functions: Going Beyond Total Rewards\n", 1297 | " - paper [2338/1898]: Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks\n", 1298 | " - paper [2340/1898]: KFC: A Scalable Approximation Algorithm for $k$−center Fair Clustering\n", 1299 | " - paper [2342/1898]: Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms\n", 1300 | " - paper [2344/1898]: Learning the Linear Quadratic Regulator from Nonlinear Observations\n", 1301 | " - paper [2346/1898]: Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate\n", 1302 | " - paper [2348/1898]: Scalable Graph Neural Networks via Bidirectional Propagation\n", 1303 | " - paper [2350/1898]: Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning\n", 1304 | " - paper [2352/1898]: Assisted Learning: A Framework for Multi-Organization Learning\n", 1305 | " - paper [2354/1898]: The Strong Screening Rule for SLOPE\n", 1306 | " - paper [2356/1898]: STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks\n", 1307 | " - paper [2358/1898]: Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks\n", 1308 | " - paper [2360/1898]: Reducing Adversarially Robust Learning to Non-Robust PAC Learning\n", 1309 | " - paper [2362/1898]: Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples\n", 1310 | " - paper [2364/1898]: Black-Box Optimization with Local Generative Surrogates\n", 1311 | " - paper [2366/1898]: Efficient Generation of Structured Objects with Constrained Adversarial Networks\n", 1312 | " - paper [2368/1898]: Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning\n", 1313 | " - paper [2370/1898]: Recovery of sparse linear classifiers from mixture of responses\n", 1314 | " - paper [2372/1898]: Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning\n", 1315 | " - paper [2374/1898]: A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints\n", 1316 | " - paper [2376/1898]: Learning Sparse Prototypes for Text Generation\n", 1317 | " - paper [2378/1898]: Implicit Rank-Minimizing Autoencoder\n", 1318 | " - paper [2380/1898]: Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning\n", 1319 | " - paper [2382/1898]: Task-Oriented Feature Distillation\n", 1320 | " - paper [2384/1898]: Entropic Causal Inference: Identifiability and Finite Sample Results\n", 1321 | " - paper [2386/1898]: Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement\n", 1322 | " - paper [2388/1898]: Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis\n", 1323 | " - paper [2390/1898]: AdaTune: Adaptive Tensor Program Compilation Made Efficient\n", 1324 | " - paper [2392/1898]: When Do Neural Networks Outperform Kernel Methods?\n", 1325 | " - paper [2394/1898]: STEER : Simple Temporal Regularization For Neural ODE\n", 1326 | " - paper [2396/1898]: A Variational Approach for Learning from Positive and Unlabeled Data\n", 1327 | " - paper [2398/1898]: Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut\n", 1328 | " - paper [2400/1898]: Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations\n", 1329 | " - paper [2402/1898]: Coresets via Bilevel Optimization for Continual Learning and Streaming\n", 1330 | " - paper [2404/1898]: Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs\n", 1331 | " - paper [2406/1898]: Understanding and Exploring the Network with Stochastic Architectures\n", 1332 | " - paper [2408/1898]: All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation\n", 1333 | " - paper [2410/1898]: Deep Evidential Regression\n", 1334 | " - paper [2412/1898]: Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks\n", 1335 | " - paper [2414/1898]: Bayesian Pseudocoresets\n", 1336 | " - paper [2416/1898]: See, Hear, Explore: Curiosity via Audio-Visual Association\n", 1337 | " - paper [2418/1898]: Adversarial Training is a Form of Data-dependent Operator Norm Regularization\n", 1338 | " - paper [2420/1898]: A Biologically Plausible Neural Network for Slow Feature Analysis\n", 1339 | " - paper [2422/1898]: Learning Feature Sparse Principal Subspace\n", 1340 | " - paper [2424/1898]: Online Adaptation for Consistent Mesh Reconstruction in the Wild\n", 1341 | " - paper [2426/1898]: Online learning with dynamics: A minimax perspective\n", 1342 | " - paper [2428/1898]: Learning to Select Best Forecast Tasks for Clinical Outcome Prediction\n", 1343 | " - paper [2430/1898]: Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping\n", 1344 | " - paper [2432/1898]: Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach\n", 1345 | " - paper [2434/1898]: From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering\n", 1346 | " - paper [2436/1898]: The Autoencoding Variational Autoencoder\n", 1347 | " - paper [2438/1898]: A Fair Classifier Using Kernel Density Estimation\n", 1348 | " - paper [2440/1898]: A Randomized Algorithm to Reduce the Support of Discrete Measures\n", 1349 | " - paper [2442/1898]: Distributionally Robust Federated Averaging\n", 1350 | " - paper [2444/1898]: Sharp uniform convergence bounds through empirical centralization\n", 1351 | " - paper [2446/1898]: COBE: Contextualized Object Embeddings from Narrated Instructional Video\n", 1352 | " - paper [2448/1898]: Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control\n", 1353 | " - paper [2450/1898]: Finite Versus Infinite Neural Networks: an Empirical Study\n", 1354 | " - paper [2452/1898]: Supermasks in Superposition\n", 1355 | " - paper [2454/1898]: Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors\n", 1356 | " - paper [2456/1898]: Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition\n", 1357 | " - paper [2458/1898]: Learning to Incentivize Other Learning Agents\n", 1358 | " - paper [2460/1898]: Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation\n", 1359 | " - paper [2462/1898]: Distributionally Robust Local Non-parametric Conditional Estimation\n", 1360 | " - paper [2464/1898]: Robust Multi-Object Matching via Iterative Reweighting of the Graph Connection Laplacian\n", 1361 | " - paper [2466/1898]: Meta-Gradient Reinforcement Learning with an Objective Discovered Online\n", 1362 | " - paper [2468/1898]: Learning Strategy-Aware Linear Classifiers\n", 1363 | " - paper [2470/1898]: Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss\n", 1364 | " - paper [2472/1898]: Calibrating Deep Neural Networks using Focal Loss\n", 1365 | " - paper [2474/1898]: Optimizing Mode Connectivity via Neuron Alignment\n", 1366 | " - paper [2476/1898]: Information Theoretic Regret Bounds for Online Nonlinear Control\n", 1367 | " - paper [2478/1898]: A kernel test for quasi-independence\n", 1368 | " - paper [2480/1898]: First Order Constrained Optimization in Policy Space\n", 1369 | " - paper [2482/1898]: Learning Augmented Energy Minimization via Speed Scaling\n", 1370 | " - paper [2484/1898]: Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning\n", 1371 | " - paper [2486/1898]: Deep Rao-Blackwellised Particle Filters for Time Series Forecasting\n", 1372 | " - paper [2488/1898]: Why are Adaptive Methods Good for Attention Models?\n", 1373 | " - paper [2490/1898]: Neural Sparse Representation for Image Restoration\n", 1374 | " - paper [2492/1898]: Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates\n", 1375 | " - paper [2494/1898]: Robust Sequence Submodular Maximization\n", 1376 | " - paper [2496/1898]: Certified Monotonic Neural Networks\n", 1377 | " - paper [2498/1898]: System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina\n", 1378 | " - paper [2500/1898]: Efficient Algorithms for Device Placement of DNN Graph Operators\n", 1379 | " - paper [2502/1898]: Active Invariant Causal Prediction: Experiment Selection through Stability\n", 1380 | " - paper [2504/1898]: BOSS: Bayesian Optimization over String Spaces\n", 1381 | " - paper [2506/1898]: Model Interpretability through the lens of Computational Complexity\n", 1382 | " - paper [2508/1898]: Markovian Score Climbing: Variational Inference with KL(p||q)\n" 1383 | ] 1384 | }, 1385 | { 1386 | "name": "stdout", 1387 | "output_type": "stream", 1388 | "text": [ 1389 | " - paper [2510/1898]: Improved Analysis of Clipping Algorithms for Non-convex Optimization\n", 1390 | " - paper [2512/1898]: Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs\n", 1391 | " - paper [2514/1898]: A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection\n", 1392 | " - paper [2516/1898]: StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks\n", 1393 | " - paper [2518/1898]: A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms\n", 1394 | " - paper [2520/1898]: Kernel Alignment Risk Estimator: Risk Prediction from Training Data\n", 1395 | " - paper [2522/1898]: Calibrating CNNs for Lifelong Learning\n", 1396 | " - paper [2524/1898]: Online Convex Optimization Over Erdos-Renyi Random Networks\n", 1397 | " - paper [2526/1898]: Robustness of Bayesian Neural Networks to Gradient-Based Attacks\n", 1398 | " - paper [2528/1898]: Parametric Instance Classification for Unsupervised Visual Feature learning\n", 1399 | " - paper [2530/1898]: Sparse Weight Activation Training\n", 1400 | " - paper [2532/1898]: Collapsing Bandits and Their Application to Public Health Intervention\n", 1401 | " - paper [2534/1898]: Neural Sparse Voxel Fields\n", 1402 | " - paper [2536/1898]: A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding\n", 1403 | " - paper [2538/1898]: The Discrete Gaussian for Differential Privacy\n", 1404 | " - paper [2540/1898]: Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing\n", 1405 | " - paper [2542/1898]: Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes\n", 1406 | " - paper [2544/1898]: Learning efficient task-dependent representations with synaptic plasticity\n", 1407 | " - paper [2546/1898]: A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions\n", 1408 | " - paper [2548/1898]: Error Bounds of Imitating Policies and Environments\n", 1409 | " - paper [2550/1898]: Disentangling Human Error from Ground Truth in Segmentation of Medical Images\n", 1410 | " - paper [2552/1898]: Consequences of Misaligned AI\n", 1411 | " - paper [2554/1898]: Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning\n", 1412 | " - paper [2556/1898]: Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences\n", 1413 | " - paper [2558/1898]: Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics\n", 1414 | " - paper [2560/1898]: Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs\n", 1415 | " - paper [2562/1898]: Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses\n", 1416 | " - paper [2564/1898]: The Lottery Ticket Hypothesis for Pre-trained BERT Networks\n", 1417 | " - paper [2566/1898]: Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity\n", 1418 | " - paper [2568/1898]: Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples\n", 1419 | " - paper [2570/1898]: AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows\n", 1420 | " - paper [2572/1898]: Few-shot Image Generation with Elastic Weight Consolidation\n", 1421 | " - paper [2574/1898]: On the Expressiveness of Approximate Inference in Bayesian Neural Networks\n", 1422 | " - paper [2576/1898]: Non-Crossing Quantile Regression for Distributional Reinforcement Learning\n", 1423 | " - paper [2578/1898]: Dark Experience for General Continual Learning: a Strong, Simple Baseline\n", 1424 | " - paper [2580/1898]: Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping\n", 1425 | " - paper [2582/1898]: Neural encoding with visual attention\n", 1426 | " - paper [2584/1898]: On the linearity of large non-linear models: when and why the tangent kernel is constant\n", 1427 | " - paper [2586/1898]: PLLay: Efficient Topological Layer based on Persistent Landscapes\n", 1428 | " - paper [2588/1898]: Decentralized Langevin Dynamics for Bayesian Learning\n", 1429 | " - paper [2590/1898]: Shared Space Transfer Learning for analyzing multi-site fMRI data\n", 1430 | " - paper [2592/1898]: The Diversified Ensemble Neural Network\n", 1431 | " - paper [2594/1898]: Inductive Quantum Embedding\n", 1432 | " - paper [2596/1898]: Variational Bayesian Unlearning\n", 1433 | " - paper [2598/1898]: Batched Coarse Ranking in Multi-Armed Bandits\n", 1434 | " - paper [2600/1898]: Understanding and Improving Fast Adversarial Training\n", 1435 | " - paper [2602/1898]: Coded Sequential Matrix Multiplication For Straggler Mitigation\n", 1436 | " - paper [2604/1898]: Attack of the Tails: Yes, You Really Can Backdoor Federated Learning\n", 1437 | " - paper [2606/1898]: Certifiably Adversarially Robust Detection of Out-of-Distribution Data\n", 1438 | " - paper [2608/1898]: Domain Generalization via Entropy Regularization\n", 1439 | " - paper [2610/1898]: Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels\n", 1440 | " - paper [2612/1898]: Skeleton-bridged Point Completion: From Global Inference to Local Adjustment\n", 1441 | " - paper [2614/1898]: Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding\n", 1442 | " - paper [2616/1898]: Improved Guarantees for k-means++ and k-means++ Parallel\n", 1443 | " - paper [2618/1898]: Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning\n", 1444 | " - paper [2620/1898]: An Efficient Adversarial Attack for Tree Ensembles\n", 1445 | " - paper [2622/1898]: Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations\n", 1446 | " - paper [2624/1898]: Online Bayesian Persuasion\n", 1447 | " - paper [2626/1898]: Robust Pre-Training by Adversarial Contrastive Learning\n", 1448 | " - paper [2628/1898]: Random Walk Graph Neural Networks\n", 1449 | " - paper [2630/1898]: Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling\n", 1450 | " - paper [2632/1898]: Fast and Accurate $k$-means++ via Rejection Sampling\n", 1451 | " - paper [2634/1898]: Variational Amodal Object Completion\n", 1452 | " - paper [2636/1898]: When Counterpoint Meets Chinese Folk Melodies\n", 1453 | " - paper [2638/1898]: Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces\n", 1454 | " - paper [2640/1898]: Universal Domain Adaptation through Self Supervision\n", 1455 | " - paper [2642/1898]: Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning​\n", 1456 | " - paper [2644/1898]: Stochastic Normalization\n", 1457 | " - paper [2646/1898]: Constrained episodic reinforcement learning in concave-convex and knapsack settings\n", 1458 | " - paper [2648/1898]: On Learning Ising Models under Huber's Contamination Model\n", 1459 | " - paper [2650/1898]: Cross-validation Confidence Intervals for Test Error\n", 1460 | " - paper [2652/1898]: DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation\n", 1461 | " - paper [2654/1898]: Bayesian Attention Modules\n", 1462 | " - paper [2656/1898]: Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations\n", 1463 | " - paper [2658/1898]: SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds\n", 1464 | " - paper [2660/1898]: A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network\n", 1465 | " - paper [2662/1898]: Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough\n", 1466 | " - paper [2664/1898]: Path Integral Based Convolution and Pooling for Graph Neural Networks\n", 1467 | " - paper [2666/1898]: Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks\n", 1468 | " - paper [2668/1898]: Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings\n", 1469 | " - paper [2670/1898]: Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds\n", 1470 | " - paper [2672/1898]: Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning\n", 1471 | " - paper [2674/1898]: GAN Memory with No Forgetting\n", 1472 | " - paper [2676/1898]: Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games\n", 1473 | " - paper [2678/1898]: Gaussian Gated Linear Networks\n", 1474 | " - paper [2680/1898]: Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding\n", 1475 | " - paper [2682/1898]: Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning\n", 1476 | " - paper [2684/1898]: Convex optimization based on global lower second-order models\n", 1477 | " - paper [2686/1898]: Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition\n", 1478 | " - paper [2688/1898]: Relative gradient optimization of the Jacobian term in unsupervised deep learning\n", 1479 | " - paper [2690/1898]: Self-Supervised Visual Representation Learning from Hierarchical Grouping\n", 1480 | " - paper [2692/1898]: Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds\n" 1481 | ] 1482 | }, 1483 | { 1484 | "name": "stdout", 1485 | "output_type": "stream", 1486 | "text": [ 1487 | " - paper [2694/1898]: Explicit Regularisation in Gaussian Noise Injections\n", 1488 | " - paper [2696/1898]: Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning\n", 1489 | " - paper [2698/1898]: Finite-Time Analysis for Double Q-learning\n", 1490 | " - paper [2700/1898]: Learning to Detect Objects with a 1 Megapixel Event Camera\n", 1491 | " - paper [2702/1898]: End-to-End Learning and Intervention in Games\n", 1492 | " - paper [2704/1898]: Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms\n", 1493 | " - paper [2706/1898]: Predictive coding in balanced neural networks with noise, chaos and delays\n", 1494 | " - paper [2708/1898]: Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs\n", 1495 | " - paper [2710/1898]: On the Equivalence between Online and Private Learnability beyond Binary Classification\n", 1496 | " - paper [2712/1898]: AViD Dataset: Anonymized Videos from Diverse Countries\n", 1497 | " - paper [2714/1898]: Probably Approximately Correct Constrained Learning\n", 1498 | " - paper [2716/1898]: RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning\n", 1499 | " - paper [2718/1898]: Decisions, Counterfactual Explanations and Strategic Behavior\n", 1500 | " - paper [2720/1898]: Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample\n", 1501 | " - paper [2722/1898]: A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization\n", 1502 | " - paper [2724/1898]: Reservoir Computing meets Recurrent Kernels and Structured Transforms\n", 1503 | " - paper [2726/1898]: Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection\n", 1504 | " - paper [2728/1898]: Linear Dynamical Systems as a Core Computational Primitive\n", 1505 | " - paper [2730/1898]: Ratio Trace Formulation of Wasserstein Discriminant Analysis\n", 1506 | " - paper [2732/1898]: PAC-Bayes Analysis Beyond the Usual Bounds\n", 1507 | " - paper [2734/1898]: Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning\n", 1508 | " - paper [2736/1898]: MPNet: Masked and Permuted Pre-training for Language Understanding\n", 1509 | " - paper [2738/1898]: Reinforcement Learning with Feedback Graphs\n", 1510 | " - paper [2740/1898]: Zap Q-Learning With Nonlinear Function Approximation\n", 1511 | " - paper [2742/1898]: Lipschitz-Certifiable Training with a Tight Outer Bound\n", 1512 | " - paper [2744/1898]: Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint\n", 1513 | " - paper [2746/1898]: Conformal Symplectic and Relativistic Optimization\n", 1514 | " - paper [2748/1898]: Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class\n", 1515 | " - paper [2750/1898]: Inverting Gradients - How easy is it to break privacy in federated learning?\n", 1516 | " - paper [2752/1898]: Dynamic allocation of limited memory resources in reinforcement learning\n", 1517 | " - paper [2754/1898]: CryptoNAS: Private Inference on a ReLU Budget\n", 1518 | " - paper [2756/1898]: A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm\n", 1519 | " - paper [2758/1898]: CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation\n", 1520 | " - paper [2760/1898]: SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection\n", 1521 | " - paper [2762/1898]: Design Space for Graph Neural Networks\n", 1522 | " - paper [2764/1898]: HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis\n", 1523 | " - paper [2766/1898]: Unbalanced Sobolev Descent \n", 1524 | " - paper [2768/1898]: Identifying Mislabeled Data using the Area Under the Margin Ranking\n", 1525 | " - paper [2770/1898]: Combining Deep Reinforcement Learning and Search for Imperfect-Information Games\n", 1526 | " - paper [2772/1898]: High-Throughput Synchronous Deep RL\n", 1527 | " - paper [2774/1898]: Contrastive Learning with Adversarial Examples \n", 1528 | " - paper [2776/1898]: Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables\n", 1529 | " - paper [2778/1898]: Adversarial Sparse Transformer for Time Series Forecasting\n", 1530 | " - paper [2780/1898]: The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks\n", 1531 | " - paper [2782/1898]: CLEARER: Multi-Scale Neural Architecture Search for Image Restoration\n", 1532 | " - paper [2784/1898]: Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights\n", 1533 | " - paper [2786/1898]: Compositional Explanations of Neurons\n", 1534 | " - paper [2788/1898]: Calibrated Reliable Regression using Maximum Mean Discrepancy\n", 1535 | " - paper [2790/1898]: Directional convergence and alignment in deep learning\n", 1536 | " - paper [2792/1898]: Functional Regularization for Representation Learning: A Unified Theoretical Perspective\n", 1537 | " - paper [2794/1898]: Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits\n", 1538 | " - paper [2796/1898]: Understanding Global Feature Contributions With Additive Importance Measures\n", 1539 | " - paper [2798/1898]: Online Non-Convex Optimization with Imperfect Feedback\n", 1540 | " - paper [2800/1898]: Co-Tuning for Transfer Learning\n", 1541 | " - paper [2802/1898]: Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning\n", 1542 | " - paper [2804/1898]: Continuous Surface Embeddings\n", 1543 | " - paper [2806/1898]: Succinct and Robust Multi-Agent Communication With Temporal Message Control\n", 1544 | " - paper [2808/1898]: Big Bird: Transformers for Longer Sequences\n", 1545 | " - paper [2810/1898]: Neural Execution Engines: Learning to Execute Subroutines\n", 1546 | " - paper [2812/1898]: Random Reshuffling: Simple Analysis with Vast Improvements\n", 1547 | " - paper [2814/1898]: Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors\n", 1548 | " - paper [2816/1898]: Statistical Optimal Transport posed as Learning Kernel Embedding\n", 1549 | " - paper [2818/1898]: Dual-Resolution Correspondence Networks\n", 1550 | " - paper [2820/1898]: Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization\n", 1551 | " - paper [2822/1898]: f-Divergence Variational Inference\n", 1552 | " - paper [2824/1898]: Unfolding recurrence by Green’s functions for optimized reservoir computing\n", 1553 | " - paper [2826/1898]: The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification\n", 1554 | " - paper [2828/1898]: Disentangling by Subspace Diffusion\n", 1555 | " - paper [2830/1898]: Towards Neural Programming Interfaces\n", 1556 | " - paper [2832/1898]: Discovering Symbolic Models from Deep Learning with Inductive Biases\n", 1557 | " - paper [2834/1898]: Real World Games Look Like Spinning Tops\n", 1558 | " - paper [2836/1898]: Cooperative Heterogeneous Deep Reinforcement Learning\n", 1559 | " - paper [2838/1898]: Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization\n", 1560 | " - paper [2840/1898]: ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool\n", 1561 | " - paper [2842/1898]: Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs\n", 1562 | " - paper [2844/1898]: Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms.\n", 1563 | " - paper [2846/1898]: Applications of Common Entropy for Causal Inference\n", 1564 | " - paper [2848/1898]: SGD with shuffling: optimal rates without component convexity and large epoch requirements\n", 1565 | " - paper [2850/1898]: Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models\n", 1566 | " - paper [2852/1898]: Neural Manifold Ordinary Differential Equations\n", 1567 | " - paper [2854/1898]: CO-Optimal Transport\n", 1568 | " - paper [2856/1898]: Continuous Meta-Learning without Tasks\n", 1569 | " - paper [2858/1898]: A mathematical theory of cooperative communication\n", 1570 | " - paper [2860/1898]: Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets\n", 1571 | " - paper [2862/1898]: Learning Invariances in Neural Networks from Training Data\n", 1572 | " - paper [2864/1898]: A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods\n", 1573 | " - paper [2866/1898]: Pruning Filter in Filter\n", 1574 | " - paper [2868/1898]: Learning to Mutate with Hypergradient Guided Population\n", 1575 | " - paper [2870/1898]: A convex optimization formulation for multivariate regression\n", 1576 | " - paper [2872/1898]: Online Meta-Critic Learning for Off-Policy Actor-Critic Methods\n", 1577 | " - paper [2874/1898]: The All-or-Nothing Phenomenon in Sparse Tensor PCA\n", 1578 | " - paper [2876/1898]: Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis\n", 1579 | " - paper [2878/1898]: ARMA Nets: Expanding Receptive Field for Dense Prediction\n", 1580 | " - paper [2880/1898]: Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations\n", 1581 | " - paper [2882/1898]: SOLOv2: Dynamic and Fast Instance Segmentation\n" 1582 | ] 1583 | }, 1584 | { 1585 | "name": "stdout", 1586 | "output_type": "stream", 1587 | "text": [ 1588 | " - paper [2884/1898]: Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization\n", 1589 | " - paper [2886/1898]: Axioms for Learning from Pairwise Comparisons\n", 1590 | " - paper [2888/1898]: Continuous Regularized Wasserstein Barycenters\n", 1591 | " - paper [2890/1898]: Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting\n", 1592 | " - paper [2892/1898]: Online Multitask Learning with Long-Term Memory\n", 1593 | " - paper [2894/1898]: Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies\n", 1594 | " - paper [2896/1898]: Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting\n", 1595 | " - paper [2898/1898]: On Reward-Free Reinforcement Learning with Linear Function Approximation\n", 1596 | " - paper [2900/1898]: Robustness of Community Detection to Random Geometric Perturbations\n", 1597 | " - paper [2902/1898]: Learning outside the Black-Box: The pursuit of interpretable models\n", 1598 | " - paper [2904/1898]: Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization\n", 1599 | " - paper [2906/1898]: Robust large-margin learning in hyperbolic space\n", 1600 | " - paper [2908/1898]: Replica-Exchange Nos\\'e-Hoover Dynamics for Bayesian Learning on Large Datasets\n", 1601 | " - paper [2910/1898]: Adversarially Robust Few-Shot Learning: A Meta-Learning Approach\n", 1602 | " - paper [2912/1898]: Neural Anisotropy Directions\n", 1603 | " - paper [2914/1898]: Digraph Inception Convolutional Networks\n", 1604 | " - paper [2916/1898]: PAC-Bayesian Bound for the Conditional Value at Risk\n", 1605 | " - paper [2918/1898]: Stochastic Stein Discrepancies\n", 1606 | " - paper [2920/1898]: On the Role of Sparsity and DAG Constraints for Learning Linear DAGs\n", 1607 | " - paper [2922/1898]: Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search\n", 1608 | " - paper [2924/1898]: Fair Multiple Decision Making Through Soft Interventions\n", 1609 | " - paper [2926/1898]: Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment\n", 1610 | " - paper [2928/1898]: Learning to Play No-Press Diplomacy with Best Response Policy Iteration\n", 1611 | " - paper [2930/1898]: Inverse Learning of Symmetries\n", 1612 | " - paper [2932/1898]: DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling\n", 1613 | " - paper [2934/1898]: Distributed Newton Can Communicate Less and Resist Byzantine Workers\n", 1614 | " - paper [2936/1898]: Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees\n", 1615 | " - paper [2938/1898]: Effective Diversity in Population Based Reinforcement Learning\n", 1616 | " - paper [2940/1898]: Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data\n", 1617 | " - paper [2942/1898]: Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces\n", 1618 | " - paper [2944/1898]: Hybrid Models for Learning to Branch\n", 1619 | " - paper [2946/1898]: WoodFisher: Efficient Second-Order Approximation for Neural Network Compression\n", 1620 | " - paper [2948/1898]: Bi-level Score Matching for Learning Energy-based Latent Variable Models\n", 1621 | " - paper [2950/1898]: Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding\n", 1622 | " - paper [2952/1898]: Decision trees as partitioning machines to characterize their generalization properties\n", 1623 | " - paper [2954/1898]: Learning to Prove Theorems by Learning to Generate Theorems\n", 1624 | " - paper [2956/1898]: 3D Self-Supervised Methods for Medical Imaging\n", 1625 | " - paper [2958/1898]: Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods \n", 1626 | " - paper [2960/1898]: Worst-Case Analysis for Randomly Collected Data\n", 1627 | " - paper [2962/1898]: Truthful Data Acquisition via Peer Prediction\n", 1628 | " - paper [2964/1898]: Learning Robust Decision Policies from Observational Data\n", 1629 | " - paper [2966/1898]: Byzantine Resilient Distributed Multi-Task Learning\n", 1630 | " - paper [2968/1898]: Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting\n", 1631 | " - paper [2970/1898]: Improving model calibration with accuracy versus uncertainty optimization\n", 1632 | " - paper [2972/1898]: The Convolution Exponential and Generalized Sylvester Flows\n", 1633 | " - paper [2974/1898]: An Improved Analysis of Stochastic Gradient Descent with Momentum\n", 1634 | " - paper [2976/1898]: Precise expressions for random projections: Low-rank approximation and randomized Newton\n", 1635 | " - paper [2978/1898]: The MAGICAL Benchmark for Robust Imitation\n", 1636 | " - paper [2980/1898]: X-CAL: Explicit Calibration for Survival Analysis\n", 1637 | " - paper [2982/1898]: Decentralized Accelerated Proximal Gradient Descent\n", 1638 | " - paper [2984/1898]: Making Non-Stochastic Control (Almost) as Easy as Stochastic\n", 1639 | " - paper [2986/1898]: BERT Loses Patience: Fast and Robust Inference with Early Exit\n", 1640 | " - paper [2988/1898]: Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization\n", 1641 | " - paper [2990/1898]: BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning\n", 1642 | " - paper [2992/1898]: Regularizing Towards Permutation Invariance In Recurrent Models\n", 1643 | " - paper [2994/1898]: What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes\n", 1644 | " - paper [2996/1898]: Batch normalization provably avoids ranks collapse for randomly initialised deep networks\n", 1645 | " - paper [2998/1898]: Choice Bandits\n", 1646 | " - paper [3000/1898]: What if Neural Networks had SVDs?\n", 1647 | " - paper [3002/1898]: A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices\n", 1648 | " - paper [3004/1898]: CoMIR: Contrastive Multimodal Image Representation for Registration\n", 1649 | " - paper [3006/1898]: Ensuring Fairness Beyond the Training Data\n", 1650 | " - paper [3008/1898]: How do fair decisions fare in long-term qualification?\n", 1651 | " - paper [3010/1898]: Pre-training via Paraphrasing\n", 1652 | " - paper [3012/1898]: GCN meets GPU: Decoupling “When to Sample” from “How to Sample”\n", 1653 | " - paper [3014/1898]: Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks\n", 1654 | " - paper [3016/1898]: All your loss are belong to Bayes\n", 1655 | " - paper [3018/1898]: HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks\n", 1656 | " - paper [3020/1898]: Sample-Efficient Reinforcement Learning of Undercomplete POMDPs\n", 1657 | " - paper [3022/1898]: Non-Convex SGD Learns Halfspaces with Adversarial Label Noise\n", 1658 | " - paper [3024/1898]: A Tight Lower Bound and Efficient Reduction for Swap Regret\n", 1659 | " - paper [3026/1898]: DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction\n", 1660 | " - paper [3028/1898]: OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling\n", 1661 | " - paper [3030/1898]: Measuring Robustness to Natural Distribution Shifts in Image Classification\n", 1662 | " - paper [3032/1898]: Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference\n", 1663 | " - paper [3034/1898]: RandAugment: Practical Automated Data Augmentation with a Reduced Search Space\n", 1664 | " - paper [3036/1898]: Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model\n", 1665 | " - paper [3038/1898]: DisARM: An Antithetic Gradient Estimator for Binary Latent Variables\n", 1666 | " - paper [3040/1898]: Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings\n", 1667 | " - paper [3042/1898]: Supervised Contrastive Learning\n", 1668 | " - paper [3044/1898]: Learning Optimal Representations with the Decodable Information Bottleneck\n", 1669 | " - paper [3046/1898]: Meta-trained agents implement Bayes-optimal agents\n", 1670 | " - paper [3048/1898]: Learning Agent Representations for Ice Hockey\n", 1671 | " - paper [3050/1898]: Weak Form Generalized Hamiltonian Learning\n", 1672 | " - paper [3052/1898]: Neural Non-Rigid Tracking\n", 1673 | " - paper [3054/1898]: Collegial Ensembles\n", 1674 | " - paper [3056/1898]: ICNet: Intra-saliency Correlation Network for Co-Saliency Detection\n", 1675 | " - paper [3058/1898]: Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows\n", 1676 | " - paper [3060/1898]: Deep Metric Learning with Spherical Embedding\n", 1677 | " - paper [3062/1898]: Preference-based Reinforcement Learning with Finite-Time Guarantees\n", 1678 | " - paper [3064/1898]: AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients\n", 1679 | " - paper [3066/1898]: Interpretable Sequence Learning for Covid-19 Forecasting\n", 1680 | " - paper [3068/1898]: Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding\n", 1681 | " - paper [3070/1898]: Modern Hopfield Networks and Attention for Immune Repertoire Classification\n" 1682 | ] 1683 | }, 1684 | { 1685 | "name": "stdout", 1686 | "output_type": "stream", 1687 | "text": [ 1688 | " - paper [3072/1898]: One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers\n", 1689 | " - paper [3074/1898]: Task-Robust Model-Agnostic Meta-Learning\n", 1690 | " - paper [3076/1898]: R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making\n", 1691 | " - paper [3078/1898]: Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity\n", 1692 | " - paper [3080/1898]: Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev\n", 1693 | " - paper [3082/1898]: Tensor Completion Made Practical\n", 1694 | " - paper [3084/1898]: Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks\n", 1695 | " - paper [3086/1898]: Content Provider Dynamics and Coordination in Recommendation Ecosystems\n", 1696 | " - paper [3088/1898]: Almost Surely Stable Deep Dynamics\n", 1697 | " - paper [3090/1898]: Experimental design for MRI by greedy policy search\n", 1698 | " - paper [3092/1898]: Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation\n", 1699 | " - paper [3094/1898]: ColdGANs: Taming Language GANs with Cautious Sampling Strategies\n", 1700 | " - paper [3096/1898]: Hedging in games: Faster convergence of external and swap regrets\n", 1701 | " - paper [3098/1898]: The Origins and Prevalence of Texture Bias in Convolutional Neural Networks\n", 1702 | " - paper [3100/1898]: Time-Reversal Symmetric ODE Network\n", 1703 | " - paper [3102/1898]: Provable Overlapping Community Detection in Weighted Graphs\n", 1704 | " - paper [3104/1898]: Fast Unbalanced Optimal Transport on a Tree\n", 1705 | " - paper [3106/1898]: Acceleration with a Ball Optimization Oracle\n", 1706 | " - paper [3108/1898]: Avoiding Side Effects By Considering Future Tasks\n", 1707 | " - paper [3110/1898]: Handling Missing Data with Graph Representation Learning\n", 1708 | " - paper [3112/1898]: Improving Auto-Augment via Augmentation-Wise Weight Sharing\n", 1709 | " - paper [3114/1898]: MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles\n", 1710 | " - paper [3116/1898]: HRN: A Holistic Approach to One Class Learning\n", 1711 | " - paper [3118/1898]: The Generalized Lasso with Nonlinear Observations and Generative Priors\n", 1712 | " - paper [3120/1898]: Fair regression via plug-in estimator and recalibration with statistical guarantees\n", 1713 | " - paper [3122/1898]: Modeling Shared responses in Neuroimaging Studies through MultiView ICA\n", 1714 | " - paper [3124/1898]: Efficient Planning in Large MDPs with Weak Linear Function Approximation\n", 1715 | " - paper [3126/1898]: Efficient Learning of Generative Models via Finite-Difference Score Matching\n", 1716 | " - paper [3128/1898]: Semialgebraic Optimization for Lipschitz Constants of ReLU Networks\n", 1717 | " - paper [3130/1898]: Linear-Sample Learning of Low-Rank Distributions\n", 1718 | " - paper [3132/1898]: Transferable Calibration with Lower Bias and Variance in Domain Adaptation\n", 1719 | " - paper [3134/1898]: Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics\n", 1720 | " - paper [3136/1898]: Online Bayesian Goal Inference for Boundedly Rational Planning Agents\n", 1721 | " - paper [3138/1898]: BayReL: Bayesian Relational Learning for Multi-omics Data Integration\n", 1722 | " - paper [3140/1898]: Weakly Supervised Deep Functional Maps for Shape Matching\n", 1723 | " - paper [3142/1898]: Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift\n", 1724 | " - paper [3144/1898]: Rethinking the Value of Labels for Improving Class-Imbalanced Learning\n", 1725 | " - paper [3146/1898]: Provably Robust Metric Learning\n", 1726 | " - paper [3148/1898]: Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings\n", 1727 | " - paper [3150/1898]: COPT: Coordinated Optimal Transport on Graphs\n", 1728 | " - paper [3152/1898]: No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems\n", 1729 | " - paper [3154/1898]: Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets\n", 1730 | " - paper [3156/1898]: Self-Adaptive Training: beyond Empirical Risk Minimization\n", 1731 | " - paper [3158/1898]: Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization\n", 1732 | " - paper [3160/1898]: Near-Optimal Comparison Based Clustering\n", 1733 | " - paper [3162/1898]: Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement\n", 1734 | " - paper [3164/1898]: A new convergent variant of Q-learning with linear function approximation\n", 1735 | " - paper [3166/1898]: TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation\n", 1736 | " - paper [3168/1898]: Neural Networks with Small Weights and Depth-Separation Barriers\n", 1737 | " - paper [3170/1898]: Untangling tradeoffs between recurrence and self-attention in artificial neural networks\n", 1738 | " - paper [3172/1898]: Dual-Free Stochastic Decentralized Optimization with Variance Reduction\n", 1739 | " - paper [3174/1898]: Online Learning in Contextual Bandits using Gated Linear Networks\n", 1740 | " - paper [3176/1898]: Throughput-Optimal Topology Design for Cross-Silo Federated Learning\n", 1741 | " - paper [3178/1898]: Quantized Variational Inference\n", 1742 | " - paper [3180/1898]: Asymptotically Optimal Exact Minibatch Metropolis-Hastings\n", 1743 | " - paper [3182/1898]: Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search\n", 1744 | " - paper [3184/1898]: Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests\n", 1745 | " - paper [3186/1898]: Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks\n", 1746 | " - paper [3188/1898]: Space-Time Correspondence as a Contrastive Random Walk\n", 1747 | " - paper [3190/1898]: The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space\n", 1748 | " - paper [3192/1898]: Exponential ergodicity of mirror-Langevin diffusions\n", 1749 | " - paper [3194/1898]: An Efficient Framework for Clustered Federated Learning\n", 1750 | " - paper [3196/1898]: Autoencoders that don't overfit towards the Identity\n", 1751 | " - paper [3198/1898]: Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond\n", 1752 | " - paper [3200/1898]: Parameterized Explainer for Graph Neural Network\n", 1753 | " - paper [3202/1898]: Recursive Inference for Variational Autoencoders\n", 1754 | " - paper [3204/1898]: Flexible mean field variational inference using mixtures of non-overlapping exponential families\n", 1755 | " - paper [3206/1898]: HYDRA: Pruning Adversarially Robust Neural Networks\n", 1756 | " - paper [3208/1898]: NVAE: A Deep Hierarchical Variational Autoencoder\n", 1757 | " - paper [3210/1898]: Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory\n", 1758 | " - paper [3212/1898]: What Do Neural Networks Learn When Trained With Random Labels?\n", 1759 | " - paper [3214/1898]: Counterfactual Prediction for Bundle Treatment\n", 1760 | " - paper [3216/1898]: Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs\n", 1761 | " - paper [3218/1898]: Learning Disentangled Representations and Group Structure of Dynamical Environments\n", 1762 | " - paper [3220/1898]: Learning Linear Programs from Optimal Decisions\n", 1763 | " - paper [3222/1898]: Wisdom of the Ensemble: Improving Consistency of Deep Learning Models\n", 1764 | " - paper [3224/1898]: Universal Function Approximation on Graphs\n", 1765 | " - paper [3226/1898]: Accelerating Reinforcement Learning through GPU Atari Emulation\n", 1766 | " - paper [3228/1898]: EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning\n", 1767 | " - paper [3230/1898]: Comparator-Adaptive Convex Bandits\n", 1768 | " - paper [3232/1898]: Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs\n", 1769 | " - paper [3234/1898]: The Adaptive Complexity of Maximizing a Gross Substitutes Valuation\n", 1770 | " - paper [3236/1898]: A Robust Functional EM Algorithm for Incomplete Panel Count Data\n", 1771 | " - paper [3238/1898]: Graph Stochastic Neural Networks for Semi-supervised Learning\n", 1772 | " - paper [3240/1898]: Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition\n", 1773 | " - paper [3242/1898]: A Benchmark for Systematic Generalization in Grounded Language Understanding\n", 1774 | " - paper [3244/1898]: Weston-Watkins Hinge Loss and Ordered Partitions\n", 1775 | " - paper [3246/1898]: Reinforcement Learning with Augmented Data\n", 1776 | " - paper [3248/1898]: Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes\n", 1777 | " - paper [3250/1898]: Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning\n" 1778 | ] 1779 | }, 1780 | { 1781 | "name": "stdout", 1782 | "output_type": "stream", 1783 | "text": [ 1784 | " - paper [3252/1898]: Estimating Training Data Influence by Tracing Gradient Descent\n", 1785 | " - paper [3254/1898]: Joint Policy Search for Multi-agent Collaboration with Imperfect Information\n", 1786 | " - paper [3256/1898]: Adversarial Bandits with Corruptions: Regret Lower Bound and No-regret Algorithm\n", 1787 | " - paper [3258/1898]: Beta R-CNN: Looking into Pedestrian Detection from Another Perspective\n", 1788 | " - paper [3260/1898]: Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks\n", 1789 | " - paper [3262/1898]: Learning Retrospective Knowledge with Reverse Reinforcement Learning\n", 1790 | " - paper [3264/1898]: Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data\n", 1791 | " - paper [3266/1898]: GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs\n", 1792 | " - paper [3268/1898]: A General Large Neighborhood Search Framework for Solving Integer Linear Programs\n", 1793 | " - paper [3270/1898]: A Theoretical Framework for Target Propagation\n", 1794 | " - paper [3272/1898]: OrganITE: Optimal transplant donor organ offering using an individual treatment effect\n", 1795 | " - paper [3274/1898]: The Complete Lasso Tradeoff Diagram\n", 1796 | " - paper [3276/1898]: On the universality of deep learning\n", 1797 | " - paper [3278/1898]: Regression with reject option and application to kNN\n", 1798 | " - paper [3280/1898]: The Primal-Dual method for Learning Augmented Algorithms\n", 1799 | " - paper [3282/1898]: FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs\n", 1800 | " - paper [3284/1898]: A Class of Algorithms for General Instrumental Variable Models\n", 1801 | " - paper [3286/1898]: Black-Box Ripper: Copying black-box models using generative evolutionary algorithms\n", 1802 | " - paper [3288/1898]: Bayesian Optimization of Risk Measures\n", 1803 | " - paper [3290/1898]: TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search\n", 1804 | " - paper [3292/1898]: GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis\n", 1805 | " - paper [3294/1898]: PIE-NET: Parametric Inference of Point Cloud Edges\n", 1806 | " - paper [3296/1898]: A Simple Language Model for Task-Oriented Dialogue\n", 1807 | " - paper [3298/1898]: A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval\n", 1808 | " - paper [3300/1898]: Confidence sequences for sampling without replacement\n", 1809 | " - paper [3302/1898]: A mean-field analysis of two-player zero-sum games\n", 1810 | " - paper [3304/1898]: Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge\n", 1811 | " - paper [3306/1898]: Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games\n", 1812 | " - paper [3308/1898]: Improving Sparse Vector Technique with Renyi Differential Privacy\n", 1813 | " - paper [3310/1898]: Latent Template Induction with Gumbel-CRFs\n", 1814 | " - paper [3312/1898]: Instance Based Approximations to Profile Maximum Likelihood\n", 1815 | " - paper [3314/1898]: Factorizable Graph Convolutional Networks\n", 1816 | " - paper [3316/1898]: Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses\n", 1817 | " - paper [3318/1898]: A Study on Encodings for Neural Architecture Search\n", 1818 | " - paper [3320/1898]: Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising\n", 1819 | " - paper [3322/1898]: Early-Learning Regularization Prevents Memorization of Noisy Labels\n", 1820 | " - paper [3324/1898]: LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond\n", 1821 | " - paper [3326/1898]: Learning Parities with Neural Networks\n", 1822 | " - paper [3328/1898]: Consistent Plug-in Classifiers for Complex Objectives and Constraints\n", 1823 | " - paper [3330/1898]: Movement Pruning: Adaptive Sparsity by Fine-Tuning\n", 1824 | " - paper [3332/1898]: Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot\n", 1825 | " - paper [3334/1898]: Online Matrix Completion with Side Information\n", 1826 | " - paper [3336/1898]: Position-based Scaled Gradient for Model Quantization and Pruning\n", 1827 | " - paper [3338/1898]: Online Learning with Primary and Secondary Losses\n", 1828 | " - paper [3340/1898]: Graph Information Bottleneck\n", 1829 | " - paper [3342/1898]: The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise\n", 1830 | " - paper [3344/1898]: Adaptive Online Estimation of Piecewise Polynomial Trends\n", 1831 | " - paper [3346/1898]: RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference\n", 1832 | " - paper [3348/1898]: Agnostic Learning with Multiple Objectives\n", 1833 | " - paper [3350/1898]: 3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data\n", 1834 | " - paper [3352/1898]: Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation\n", 1835 | " - paper [3354/1898]: Differentiable Top-k with Optimal Transport\n", 1836 | " - paper [3356/1898]: Information-theoretic Task Selection for Meta-Reinforcement Learning\n", 1837 | " - paper [3358/1898]: A Limitation of the PAC-Bayes Framework\n", 1838 | " - paper [3360/1898]: On Completeness-aware Concept-Based Explanations in Deep Neural Networks\n", 1839 | " - paper [3362/1898]: Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems\n", 1840 | " - paper [3364/1898]: Why Normalizing Flows Fail to Detect Out-of-Distribution Data\n", 1841 | " - paper [3366/1898]: Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay\n", 1842 | " - paper [3368/1898]: Unsupervised Translation of Programming Languages\n", 1843 | " - paper [3370/1898]: Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation\n", 1844 | " - paper [3372/1898]: Optimally Deceiving a Learning Leader in Stackelberg Games\n", 1845 | " - paper [3374/1898]: Online Optimization with Memory and Competitive Control\n", 1846 | " - paper [3376/1898]: IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method\n", 1847 | " - paper [3378/1898]: Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation\n", 1848 | " - paper [3380/1898]: Learning from Failure: De-biasing Classifier from Biased Classifier\n", 1849 | " - paper [3382/1898]: Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder\n", 1850 | " - paper [3384/1898]: Deep Diffusion-Invariant Wasserstein Distributional Classification\n", 1851 | " - paper [3386/1898]: Finding All $\\epsilon$-Good Arms in Stochastic Bandits\n", 1852 | " - paper [3388/1898]: Meta-Learning through Hebbian Plasticity in Random Networks\n", 1853 | " - paper [3390/1898]: A Computational Separation between Private Learning and Online Learning\n", 1854 | " - paper [3392/1898]: Top-KAST: Top-K Always Sparse Training\n", 1855 | " - paper [3394/1898]: Meta-Learning with Adaptive Hyperparameters\n", 1856 | " - paper [3396/1898]: Tight last-iterate convergence rates for no-regret learning in multi-player games\n", 1857 | " - paper [3398/1898]: Curvature Regularization to Prevent Distortion in Graph Embedding\n", 1858 | " - paper [3400/1898]: Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability\n", 1859 | " - paper [3402/1898]: Statistical and Topological Properties of Sliced Probability Divergences\n", 1860 | " - paper [3404/1898]: Probabilistic Active Meta-Learning\n", 1861 | " - paper [3406/1898]: Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher\n", 1862 | " - paper [3408/1898]: Adversarial Attacks on Deep Graph Matching\n", 1863 | " - paper [3410/1898]: The Generalization-Stability Tradeoff In Neural Network Pruning\n", 1864 | " - paper [3412/1898]: Gradient-EM Bayesian Meta-Learning\n", 1865 | " - paper [3414/1898]: Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems\n", 1866 | " - paper [3416/1898]: Linearly Converging Error Compensated SGD\n", 1867 | " - paper [3418/1898]: Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction\n", 1868 | " - paper [3420/1898]: A Self-Tuning Actor-Critic Algorithm\n", 1869 | " - paper [3422/1898]: The Cone of Silence: Speech Separation by Localization\n", 1870 | " - paper [3424/1898]: High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds\n", 1871 | " - paper [3426/1898]: Train-by-Reconnect: Decoupling Locations of Weights from Their Values\n", 1872 | " - paper [3428/1898]: Learning discrete distributions: user vs item-level privacy\n", 1873 | " - paper [3430/1898]: Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula\n", 1874 | " - paper [3432/1898]: Sparse and Continuous Attention Mechanisms\n", 1875 | " - paper [3434/1898]: Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection\n", 1876 | " - paper [3436/1898]: Learning by Minimizing the Sum of Ranked Range\n", 1877 | " - paper [3438/1898]: Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations\n" 1878 | ] 1879 | }, 1880 | { 1881 | "name": "stdout", 1882 | "output_type": "stream", 1883 | "text": [ 1884 | " - paper [3440/1898]: Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features\n", 1885 | " - paper [3442/1898]: Fair Hierarchical Clustering\n", 1886 | " - paper [3444/1898]: Self-training Avoids Using Spurious Features Under Domain Shift\n", 1887 | " - paper [3446/1898]: Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions\n", 1888 | " - paper [3448/1898]: CircleGAN: Generative Adversarial Learning across Spherical Circles\n", 1889 | " - paper [3450/1898]: WOR and $p$'s: Sketches for $\\ell_p$-Sampling Without Replacement\n", 1890 | " - paper [3452/1898]: Hypersolvers: Toward Fast Continuous-Depth Models\n", 1891 | " - paper [3454/1898]: Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment\n", 1892 | " - paper [3456/1898]: Escaping the Gravitational Pull of Softmax\n", 1893 | " - paper [3458/1898]: Regret in Online Recommendation Systems\n", 1894 | " - paper [3460/1898]: On Convergence and Generalization of Dropout Training\n", 1895 | " - paper [3462/1898]: Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking\n", 1896 | " - paper [3464/1898]: Implicit Regularization in Deep Learning May Not Be Explainable by Norms\n", 1897 | " - paper [3466/1898]: POMO: Policy Optimization with Multiple Optima for Reinforcement Learning\n", 1898 | " - paper [3468/1898]: Uncertainty-aware Self-training for Few-shot Text Classification\n", 1899 | " - paper [3470/1898]: Learning to Learn with Feedback and Local Plasticity\n", 1900 | " - paper [3472/1898]: Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization\n", 1901 | " - paper [3474/1898]: Sharper Generalization Bounds for Pairwise Learning\n", 1902 | " - paper [3476/1898]: A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings\n", 1903 | " - paper [3478/1898]: Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality\n", 1904 | " - paper [3480/1898]: Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning\n", 1905 | " - paper [3482/1898]: Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning\n", 1906 | " - paper [3484/1898]: RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor\n", 1907 | " - paper [3486/1898]: Efficient Clustering for Stretched Mixtures: Landscape and Optimality\n", 1908 | " - paper [3488/1898]: A Group-Theoretic Framework for Data Augmentation\n", 1909 | " - paper [3490/1898]: The Statistical Cost of Robust Kernel Hyperparameter Turning\n", 1910 | " - paper [3492/1898]: How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks?\n", 1911 | " - paper [3494/1898]: ContraGAN: Contrastive Learning for Conditional Image Generation\n", 1912 | " - paper [3496/1898]: On the distance between two neural networks and the stability of learning\n", 1913 | " - paper [3498/1898]: A Topological Filter for Learning with Label Noise\n", 1914 | " - paper [3500/1898]: Personalized Federated Learning with Moreau Envelopes\n", 1915 | " - paper [3502/1898]: Avoiding Side Effects in Complex Environments\n", 1916 | " - paper [3504/1898]: No-regret Learning in Price Competitions under Consumer Reference Effects\n", 1917 | " - paper [3506/1898]: Geometric Dataset Distances via Optimal Transport\n", 1918 | " - paper [3508/1898]: Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters\n", 1919 | " - paper [3510/1898]: A novel variational form of the Schatten-$p$ quasi-norm\n", 1920 | " - paper [3512/1898]: Energy-based Out-of-distribution Detection\n", 1921 | " - paper [3514/1898]: On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them\n", 1922 | " - paper [3516/1898]: User-Dependent Neural Sequence Models for Continuous-Time Event Data\n", 1923 | " - paper [3518/1898]: Active Structure Learning of Causal DAGs via Directed Clique Trees\n", 1924 | " - paper [3520/1898]: Convergence and Stability of Graph Convolutional Networks on Large Random Graphs\n", 1925 | " - paper [3522/1898]: BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization\n", 1926 | " - paper [3524/1898]: Reconsidering Generative Objectives For Counterfactual Reasoning\n", 1927 | " - paper [3526/1898]: Robust Federated Learning: The Case of Affine Distribution Shifts\n", 1928 | " - paper [3528/1898]: Quantile Propagation for Wasserstein-Approximate Gaussian Processes\n", 1929 | " - paper [3530/1898]: Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning\n", 1930 | " - paper [3532/1898]: High-contrast “gaudy” images improve the training of deep neural network models of visual cortex\n", 1931 | " - paper [3534/1898]: Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion\n", 1932 | " - paper [3536/1898]: Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms\n", 1933 | " - paper [3538/1898]: H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks\n", 1934 | " - paper [3540/1898]: Neural Unsigned Distance Fields for Implicit Function Learning\n", 1935 | " - paper [3542/1898]: Curriculum By Smoothing\n", 1936 | " - paper [3544/1898]: Fast Transformers with Clustered Attention\n", 1937 | " - paper [3546/1898]: The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification\n", 1938 | " - paper [3548/1898]: Strongly Incremental Constituency Parsing with Graph Neural Networks\n", 1939 | " - paper [3550/1898]: AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection\n", 1940 | " - paper [3552/1898]: Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation\n", 1941 | " - paper [3554/1898]: Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians\n", 1942 | " - paper [3556/1898]: First-Order Methods for Large-Scale Market Equilibrium Computation\n", 1943 | " - paper [3558/1898]: Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects\n", 1944 | " - paper [3560/1898]: Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis\n", 1945 | " - paper [3562/1898]: A General Method for Robust Learning from Batches\n", 1946 | " - paper [3564/1898]: Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning\n", 1947 | " - paper [3566/1898]: Hard Negative Mixing for Contrastive Learning\n", 1948 | " - paper [3568/1898]: MOReL: Model-Based Offline Reinforcement Learning\n", 1949 | " - paper [3570/1898]: Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings\n", 1950 | " - paper [3572/1898]: Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion\n", 1951 | " - paper [3574/1898]: Learning Semantic-aware Normalization for Generative Adversarial Networks\n", 1952 | " - paper [3576/1898]: Differentiable Causal Discovery from Interventional Data\n", 1953 | " - paper [3578/1898]: One-sample Guided Object Representation Disassembling\n", 1954 | " - paper [3580/1898]: Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate\n", 1955 | " - paper [3582/1898]: Robust Persistence Diagrams using Reproducing Kernels\n", 1956 | " - paper [3584/1898]: Contextual Games: Multi-Agent Learning with Side Information\n", 1957 | " - paper [3586/1898]: Goal-directed Generation of Discrete Structures with Conditional Generative Models\n", 1958 | " - paper [3588/1898]: Beyond Lazy Training for Over-parameterized Tensor Decomposition\n", 1959 | " - paper [3590/1898]: Denoised Smoothing: A Provable Defense for Pretrained Classifiers\n", 1960 | " - paper [3592/1898]: Minibatch Stochastic Approximate Proximal Point Methods\n", 1961 | " - paper [3594/1898]: Attribute Prototype Network for Zero-Shot Learning\n", 1962 | " - paper [3596/1898]: CrossTransformers: spatially-aware few-shot transfer\n", 1963 | " - paper [3598/1898]: Learning Latent Space Energy-Based Prior Model\n", 1964 | " - paper [3600/1898]: SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology\n", 1965 | " - paper [3602/1898]: Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation\n", 1966 | " - paper [3604/1898]: High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization\n", 1967 | " - paper [3606/1898]: Model Fusion via Optimal Transport\n", 1968 | " - paper [3608/1898]: On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems\n", 1969 | " - paper [3610/1898]: Learning Individually Inferred Communication for Multi-Agent Cooperation\n", 1970 | " - paper [3612/1898]: Set2Graph: Learning Graphs From Sets\n", 1971 | " - paper [3614/1898]: Graph Random Neural Networks for Semi-Supervised Learning on Graphs\n", 1972 | " - paper [3616/1898]: Gradient Boosted Normalizing Flows\n", 1973 | " - paper [3618/1898]: Open Graph Benchmark: Datasets for Machine Learning on Graphs\n", 1974 | " - paper [3620/1898]: Towards Understanding Hierarchical Learning: Benefits of Neural Representations\n" 1975 | ] 1976 | }, 1977 | { 1978 | "name": "stdout", 1979 | "output_type": "stream", 1980 | "text": [ 1981 | " - paper [3622/1898]: Texture Interpolation for Probing Visual Perception\n", 1982 | " - paper [3624/1898]: Hierarchical Neural Architecture Search for Deep Stereo Matching\n", 1983 | " - paper [3626/1898]: MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models\n", 1984 | " - paper [3628/1898]: Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy\n", 1985 | " - paper [3630/1898]: Focus of Attention Improves Information Transfer in Visual Features\n", 1986 | " - paper [3632/1898]: Auditing Differentially Private Machine Learning: How Private is Private SGD?\n", 1987 | " - paper [3634/1898]: A Dynamical Central Limit Theorem for Shallow Neural Networks\n", 1988 | " - paper [3636/1898]: Measuring Systematic Generalization in Neural Proof Generation with Transformers\n", 1989 | " - paper [3638/1898]: Big Self-Supervised Models are Strong Semi-Supervised Learners\n", 1990 | " - paper [3640/1898]: Learning from Label Proportions: A Mutual Contamination Framework\n", 1991 | " - paper [3642/1898]: Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization\n", 1992 | " - paper [3644/1898]: Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs\n", 1993 | " - paper [3646/1898]: Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning\n", 1994 | " - paper [3648/1898]: Model Class Reliance for Random Forests\n", 1995 | " - paper [3650/1898]: Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games\n", 1996 | " - paper [3652/1898]: Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity\n", 1997 | " - paper [3654/1898]: Learning to Adapt to Evolving Domains\n", 1998 | " - paper [3656/1898]: Synthesizing Tasks for Block-based Programming\n", 1999 | " - paper [3658/1898]: Scalable Belief Propagation via Relaxed Scheduling\n", 2000 | " - paper [3660/1898]: Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks\n", 2001 | " - paper [3662/1898]: Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret\n", 2002 | " - paper [3664/1898]: Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes\n", 2003 | " - paper [3666/1898]: Faster DBSCAN via subsampled similarity queries\n", 2004 | " - paper [3668/1898]: De-Anonymizing Text by Fingerprinting Language Generation\n", 2005 | " - paper [3670/1898]: Multiparameter Persistence Image for Topological Machine Learning\n", 2006 | " - paper [3672/1898]: PLANS: Neuro-Symbolic Program Learning from Videos\n", 2007 | " - paper [3674/1898]: Matrix Inference and Estimation in Multi-Layer Models\n", 2008 | " - paper [3676/1898]: MeshSDF: Differentiable Iso-Surface Extraction\n", 2009 | " - paper [3678/1898]: Variational Interaction Information Maximization for Cross-domain Disentanglement\n", 2010 | " - paper [3680/1898]: Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning\n", 2011 | " - paper [3682/1898]: Faithful Embeddings for Knowledge Base Queries\n", 2012 | " - paper [3684/1898]: Wasserstein Distances for Stereo Disparity Estimation\n", 2013 | " - paper [3686/1898]: Multi-agent Trajectory Prediction with Fuzzy Query Attention\n", 2014 | " - paper [3688/1898]: Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping\n", 2015 | " - paper [3690/1898]: An Analysis of SVD for Deep Rotation Estimation\n", 2016 | " - paper [3692/1898]: Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks\n", 2017 | " - paper [3694/1898]: Manifold GPLVMs for discovering non-Euclidean latent structure in neural data\n", 2018 | " - paper [3696/1898]: Distributed Distillation for On-Device Learning\n", 2019 | " - paper [3698/1898]: COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning\n", 2020 | " - paper [3700/1898]: Passport-aware Normalization for Deep Model Protection\n", 2021 | " - paper [3702/1898]: Sampling-Decomposable Generative Adversarial Recommender\n", 2022 | " - paper [3704/1898]: Limits to Depth Efficiencies of Self-Attention\n" 2023 | ] 2024 | }, 2025 | { 2026 | "ename": "RecursionError", 2027 | "evalue": "maximum recursion depth exceeded", 2028 | "output_type": "error", 2029 | "traceback": [ 2030 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 2031 | "\u001b[0;31mRecursionError\u001b[0m Traceback (most recent call last)", 2032 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0mconf_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconf_year\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpaper_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'neurips_conf_data.pkl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconf_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHIGHEST_PROTOCOL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 2033 | "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded" 2034 | ] 2035 | } 2036 | ], 2037 | "source": [ 2038 | "\n", 2039 | "# Loop through all conference years\n", 2040 | "all_conferences = [cc for cc in soup.find_all('li') if 'paper' in cc.a.get('href')]\n", 2041 | "all_conferences = all_conferences[::-1]\n", 2042 | "\n", 2043 | "offset_conf = len(conf_data)\n", 2044 | "for cc in all_conferences[offset_conf:]:\n", 2045 | " conf_link = urljoin(nips_papers, cc.a.get('href'))\n", 2046 | " conf_year = conf_link.split('/')[-1] \n", 2047 | " html_text = requests.get(conf_link).text\n", 2048 | " conf = BeautifulSoup(html_text, 'html.parser')\n", 2049 | " \n", 2050 | " # Loop through all current conference's papers\n", 2051 | " print(\"\\n\\nProcessing: \", cc.a.contents[0])\n", 2052 | " paper_list = []\n", 2053 | " offset_papers = len(paper_list)\n", 2054 | " all_papers = [pp for pp in conf.find_all('li') if 'paper' in pp.a.get('href')]\n", 2055 | " \n", 2056 | " for pi, pp in enumerate(all_papers[offset_papers:]):\n", 2057 | " \n", 2058 | " # Get paper info\n", 2059 | " print(\" - paper [{}/{}]: {}\".format(pi + 1 + offset_papers, len(all_papers), pp.a.contents[0]))\n", 2060 | " paper_link = urljoin(conf_link, pp.a.get('href')) \n", 2061 | " html_paper = requests.get(paper_link).text\n", 2062 | " \n", 2063 | " # Extract paper metadata\n", 2064 | " if \"Metadata\" in html_paper:\n", 2065 | " link_file = paper_link.replace('hash', 'file')\n", 2066 | " link_meta = link_file.replace('html', 'json')\n", 2067 | " link_meta = link_meta.replace('Abstract', 'Metadata')\n", 2068 | " html_text = requests.get(link_meta).text\n", 2069 | " if html_text == 'Resource Not Found':\n", 2070 | " conf = BeautifulSoup(html_paper, 'html.parser')\n", 2071 | " abstract_text = conf.find('h4', string='Abstract').next_sibling.next_sibling.contents[0]\n", 2072 | " abstract = None if abstract_text == 'Abstract Unavailable' else abstract_text\n", 2073 | " abstract = abstract.replace('

', '')\n", 2074 | " abstract = abstract.replace('

', '')\n", 2075 | " abstract = abstract.replace('\\n', ' ')\n", 2076 | " author_list = [\n", 2077 | " {'given_name': aa.split(' ')[0],\n", 2078 | " 'family_name': aa.split(' ')[1],\n", 2079 | " 'institution': None} for aa in pp.i.contents[0].split(', ')]\n", 2080 | " paper_meta = {\n", 2081 | " 'title': str(pp.a.contents[0]),\n", 2082 | " 'authors': author_list,\n", 2083 | " 'abstract': abstract,\n", 2084 | " 'full_text': None} \n", 2085 | " else:\n", 2086 | " paper_meta = json.loads(html_text)\n", 2087 | " if 'full_text' in paper_meta.keys():\n", 2088 | " paper_meta['full_text'] = paper_meta['full_text'].replace('\\n', ' ')\n", 2089 | " else:\n", 2090 | " # make nicer not copy\n", 2091 | " conf = BeautifulSoup(html_paper, 'html.parser')\n", 2092 | " abstract_text = conf.find('h4', string='Abstract').next_sibling.next_sibling.contents[0]\n", 2093 | " abstract = None if abstract_text == 'Abstract Unavailable' else str(abstract_text)\n", 2094 | " abstract = abstract.replace('

', '')\n", 2095 | " abstract = abstract.replace('

', '')\n", 2096 | " abstract = abstract.replace('\\n', ' ')\n", 2097 | " author_list = [\n", 2098 | " {'given_name': aa.split(' ')[0],\n", 2099 | " 'family_name': aa.split(' ')[1],\n", 2100 | " 'institution': None} for aa in pp.i.contents[0].split(', ')]\n", 2101 | " paper_meta = {\n", 2102 | " 'title': str(pp.a.contents[0]),\n", 2103 | " 'authors': author_list,\n", 2104 | " 'abstract': abstract,\n", 2105 | " 'full_text': None}\n", 2106 | " \n", 2107 | " # Extract paper supplemental\n", 2108 | " if \"Supplemental\" in html_paper:\n", 2109 | " has_supplement = True\n", 2110 | " else:\n", 2111 | " has_supplement = False\n", 2112 | " \n", 2113 | " # Extract paper reviews\n", 2114 | " if \"Reviews\" in html_paper:\n", 2115 | " link_file = paper_link.replace('hash', 'file')\n", 2116 | " link_review = link_file.replace('Abstract', 'Reviews')\n", 2117 | " html_text = requests.get(link_review).text\n", 2118 | " reviews = get_reviews(html_text)\n", 2119 | " else:\n", 2120 | " reviews = None\n", 2121 | " \n", 2122 | " # Extract scholar citation data\n", 2123 | " num_cit = get_num_citations(title=paper_meta['title'], authors=paper_meta['authors'], year=conf_year)\n", 2124 | " \n", 2125 | " # Update paper info\n", 2126 | " paper_meta.update({\n", 2127 | " 'year': conf_year,\n", 2128 | " 'citations': num_cit,\n", 2129 | " 'institutions': list(set([aa['institution'] for aa in paper_meta['authors']])),\n", 2130 | " 'reviews': reviews,\n", 2131 | " 'has_supplement': has_supplement})\n", 2132 | " paper_list.append(paper_meta)\n", 2133 | " \n", 2134 | " \n", 2135 | " # Update conference info and save to file\n", 2136 | " conf_data[conf_year] = paper_list\n", 2137 | " with open('../data/neurips_conf_data.pkl', 'wb') as handle:\n", 2138 | " pickle.dump(conf_data, handle, protocol=pickle.HIGHEST_PROTOCOL)\n" 2139 | ] 2140 | }, 2141 | { 2142 | "cell_type": "code", 2143 | "execution_count": null, 2144 | "metadata": { 2145 | "collapsed": true 2146 | }, 2147 | "outputs": [], 2148 | "source": [ 2149 | "\n" 2150 | ] 2151 | } 2152 | ], 2153 | "metadata": { 2154 | "kernelspec": { 2155 | "display_name": "Python 3", 2156 | "language": "python", 2157 | "name": "python3" 2158 | }, 2159 | "language_info": { 2160 | "codemirror_mode": { 2161 | "name": "ipython", 2162 | "version": 3 2163 | }, 2164 | "file_extension": ".py", 2165 | "mimetype": "text/x-python", 2166 | "name": "python", 2167 | "nbconvert_exporter": "python", 2168 | "pygments_lexer": "ipython3", 2169 | "version": "3.5.6" 2170 | } 2171 | }, 2172 | "nbformat": 4, 2173 | "nbformat_minor": 4 2174 | } 2175 | --------------------------------------------------------------------------------