└── README.md


/README.md:
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 1 | # Fast-Furious-Paper
 2 | Reading Group @ DMG
 3 | 
 4 | # About
 5 | 
 6 | ## House Rules
 7 | - Everyone needs to present & attend;
 8 | - Four papers a week (four presenters);
 9 | - TIme & Location: Wed @ SC 2124 (or 2102 if occupied).
10 | 
11 | ## How to add a paper?
12 | - Modify this readme file, but instead of committing directly to the `master` branch, please create a new branch and start a pull request (like a proposal).
13 | - Everyone can only submit pull requests or merge pull requests *from others* (every proposal needs a second).
14 | - The order of presentations would be decided in the alphabet order (of presenters).
15 | 
16 | **IMPORTANT:** you need to **approve** before merging the pull request.
17 | 
18 | # Presenter List
19 | 
20 | - Jiaxin Huang
21 | - Jieyu Zhang
22 | - Jingbo Shang
23 | - Liyuan Liu
24 | - Qi Zhu
25 | - Shi Zhi
26 | - Xiaotao Gu
27 | - Xingyu Fu
28 | - Xinyang Zhang
29 | - Yu Zhang
30 | - Zihan Wang
31 | - Zoey Li
32 | 
33 | # Paper List
34 | 
35 | - [RESIDUAL GATED GRAPH CONVNETS](https://arxiv.org/pdf/1711.07553.pdf): Xingyu Fu
36 | - [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://arxiv.org/abs/1503.00075): Xinyang Zhang
37 | - [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://arxiv.org/pdf/1708.03743.pdf): Yu Zhang
38 | - [GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models](https://arxiv.org/pdf/1802.08773.pdf): Jieyu Zhang
39 | - [The Unreasonable Effectiveness of (Zero) Initialization in Deep Residual Learning](https://openreview.net/pdf?id=H1gsz30cKX)
40 | 
41 | # Presented
42 | 
43 | - [mixup: BEYOND EMPIRICAL RISK MINIMIZATION](https://arxiv.org/pdf/1710.09412.pdf)
44 | - [Dropout is a special case of the stochastic delta rule: faster and more accurate deep learning](https://arxiv.org/pdf/1808.03578.pdf)
45 | - [Excitation Dropout: Encouraging Plasticity in Deep Neural Networks](https://openreview.net/pdf?id=H1xQSjCqFQ)
46 | - [Neural Cross-lingual Named Entity Recognition with Minimal Resources](https://arxiv.org/pdf/1808.09861.pdf)
47 | - [Multi-View Intact Space Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7072521)
48 | - [Co-teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels](https://arxiv.org/abs/1804.06872)
49 | - [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://arxiv.org/abs/1712.05055)
50 | - [Essentially No Barriers in Neural Network Energy Landscape](https://arxiv.org/pdf/1803.00885.pdf)
51 | - [Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent](https://arxiv.org/abs/1711.07950)
52 | - [How Powerful Are Graph Neural Networks?](https://arxiv.org/abs/1810.00826)
53 | - [Relational inductive biases, deep learning, and graph networks](https://arxiv.org/pdf/1806.01261.pdf): Fang Guo
54 | - [GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations](https://arxiv.org/abs/1806.05662): Qi Zhu
55 | - [Labeling Gaps Between Words:Recognizing Overlapping Mentions with Mention Separators](http://www.statnlp.org/research/ie/emnlp2017-mention-separators.pdf): Shi Zhi
56 | - [Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry](https://arxiv.org/pdf/1806.03417.pdf): Xiaotao Gu
57 | - [Nested Named Entity Recognition Revisited](http://aclweb.org/anthology/N18-1079): Liyuan Liu
58 | - [A Neural Layered Model for Nested Named Entity Recognition](http://aclweb.org/anthology/N18-1131): Liyuan Liu
59 | - [A Neural Layered Model for Nested Named Entity Recognition](http://aclweb.org/anthology/N18-1131): Liyuan Liu
60 | - [A Neural Transition-based Model for Nested Mention Recognition](https://arxiv.org/pdf/1810.01808.pdf): Liyuan Liu
61 | - [DARTS: Differentiable Architecture Search](https://openreview.net/forum?id=S1eYHoC5FX&noteId=rkxzCbNziX): Zihan Wang
62 | 


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