├── .gitignore ├── LICENSE └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Ignito 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 | # ML/AI Research Papers Solved 2 | This repository contains everything you need to become proficient in ML/AI Research and Research Papers. 3 | 4 | [How to Make Best Use of ML/DL Research Papers?](https://open.substack.com/pub/naina0405/p/how-to-make-best-use-of-mldl-research?r=14q3sp&utm_campaign=post&utm_medium=web) 5 | 6 | 7 | ![FuSPZdvWYAImbnA](https://github.com/Coder-World04/ML-AI-Research-Papers---Solved/assets/104568275/cb2c8cbc-af23-4205-963c-8a50ede4fa5a) 8 | 9 | Pic credits: ResearchGate 10 | 11 | Youtube for all the implemented projects and tech interview resources - [Ignito Youtube Channel](https://www.youtube.com/@ignito5917/about) 12 | 13 | [Complete Cheat Sheet for Tech Interviews - How to prepare efficiently](https://open.substack.com/pub/naina0405/p/cheat-sheet-for-tech-interviews-how-339?r=14q3sp&utm_campaign=post&utm_medium=web) 14 | 15 | [I took theses Projects Based Courses to Build Industry aligned Data Science and ML skills](https://open.substack.com/pub/naina0405/p/i-took-theses-projects-based-courses-af3?r=14q3sp&utm_campaign=post&utm_medium=web) 16 | 17 | [Part 1 - How to solve Any ML System Design Problem](https://open.substack.com/pub/naina0405/p/part-1-how-to-solve-any-ml-system?r=14q3sp&utm_campaign=post&utm_medium=web) 18 | 19 | 20 | Link - [Complete ML Research Papers Summarized Series](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) 21 | 22 | We will covering each and every Research Paper using 10 step framework — 23 | 24 | 1. [Research Paper Name and Authors](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) 25 | 26 | 2. [Area and field of research ](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) 27 | 28 | 3. [Main Contributions](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) 29 | 30 | 4. [ Main Results](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) 31 | 32 | 5. [ Main Findings](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) 33 | 34 | 6. [Opportunities](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) 35 | 36 | 7. [Future Research](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) 37 | 38 | 8. [Future Projects](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) 39 | 40 | 9. [Code and Results](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) 41 | 42 | 10. [Link to the Research Paper](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) 43 | 44 | --------------- 45 | 46 | # Must know concepts before you dive in the research papers— 47 | | Model Name | Link | 48 | |---------------------------------------|--------------------------------------------------------------------------------------------------| 49 | | Transformer | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 50 | | TransformerXL | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 51 | | VGG | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 52 | | Mask RCNN | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 53 | | Masked Autoencoder | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 54 | | BEiT | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 55 | | BERT | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 56 | | ColD Fusion | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 57 | | ConvMixer | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 58 | | Deep and Cross Network | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 59 | | DenseNet | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 60 | | DistilBERT | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 61 | | DiT | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 62 | | DocFormer | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 63 | | Donut | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 64 | | EfficientNet | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 65 | | ELMo | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 66 | | Entity Embeddings | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 67 | | ERNIE-Layout | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 68 | | FastBERT | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 69 | | Fast RCNN | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 70 | | Faster RCNN | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 71 | | MobileBERT | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 72 | | MobileNetV1 | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 73 | | MobileNetV2 | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 74 | | MobileNetV3 | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 75 | | RCNN | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 76 | | ResNet | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 77 | | ResNext | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 78 | | SentenceBERT | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 79 | | Single Shot MultiBox Detector (SSD) | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 80 | | StructuralLM | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 81 | | Swin Transformer | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 82 | | TableNet | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 83 | | TabTransformer | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 84 | | Tabular ResNet | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 85 | | TinyBERT | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 86 | | Vision Transformer | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 87 | | Wide and Deep Learning | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 88 | | Xception | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 89 | | XLNet | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 90 | | AlexNet | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 91 | | BART | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 92 | | InceptionNetV2 and InceptionNetV3 | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 93 | | InceptionNetV4 and InceptionResNet | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 94 | | Layout LM | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 95 | | Layout LM v2 | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 96 | | Layout LM v3 | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 97 | | Lenet | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 98 | | LiLT | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 99 | | Feature Pyramid Network | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 100 | | Feature Tokenizer Transformer | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 101 | | Focal Loss (RetinaNet) | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 102 | 103 | ---- 104 | 105 | # NLP Research Papers 106 | 107 | | Paper Name | Simplified/Summarized Version | 108 | | --- | --- | 109 | | Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 110 | | Bag of Tricks for Efficient Text Classification | [Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 111 | |Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd) | 112 | |(QA)²: Question Answering with Questionable Assumptions|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 113 | |QueryForm: A Simple Zero-shot Form Entity Query Framework|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 114 | |Semi-supervised Sequence Learning|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 115 | |Universal Language Model Fine-tuning for Text Classification|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 116 | |DARTS: Differentiable Architecture Search|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 117 | |RoBERTa: A Robustly Optimized BERT Pretraining Approach|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 118 | |Generating Sequences With Recurrent Neural Networks|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 119 | |Deep contextualized word representations|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 120 | |Regularizing and Optimizing LSTM Language Models|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 121 | |End-To-End Memory Networks|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 122 | |Listen, Attend and Spell|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 123 | |Well-Read Students Learn Better: On the Importance of Pre-training Compact Models|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 124 | |Language Models are Few-Shot Learners|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 125 | |Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 126 | |DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 127 | |Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 128 | |LIMA: Less Is More for Alignment|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 129 | |Efficient Neural Architecture Search via Parameter Sharing|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 130 | |Tree of Thoughts: Deliberate Problem Solving with Large Language Models|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 131 | |AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 132 | |FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 133 | |CodeT5+: Open Code Large Language Models for Code Understanding and Generation|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 134 | |Unlimiformer: Long-Range Transformers with Unlimited Length Input|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 135 | |Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 136 | |PaLM: Scaling Language Modeling with Pathways|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 137 | |Attention Is All You Need|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 138 | |Denoising Diffusion Probabilistic Models|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 139 | |ZeRO: Memory Optimizations Toward Training Trillion Parameter Models|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 140 | |Wide Residual Networks|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 141 | |FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 142 | |STaR: Bootstrapping Reasoning With Reasoning|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 143 | |Meta-Gradient Reinforcement Learning|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 144 | |Distilling the Knowledge in a Neural Network|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 145 | |How to Fine-Tune BERT for Text Classification?|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 146 | |Primer: Searching for Efficient Transformers for Language Modeling|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 147 | |Training Compute-Optimal Large Language Models|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 148 | |Learning Transferable Visual Models From Natural Language Supervision|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 149 | |More Coming soon| | 150 | 151 | -------------- 152 | 153 | # CV Research Papers 154 | 155 | | Paper Name | Summarized and Simplified Version | 156 | | --- | --- | 157 | |NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis|[Link](https://medium.com/coders-mojo/complete-ml-research-papers-summarized-a69afd5bb9bf?sk=54dcfdc31cf7c959192ebf666ca24cdd)| 158 | |More Coming Soon| | 159 | 160 | 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