Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/ayushsubedi/greatpapers

An attempt to learn and summarize groundbreaking papers in ml/ai/ds
https://github.com/ayushsubedi/greatpapers

Last synced: 9 days ago
JSON representation

An attempt to learn and summarize groundbreaking papers in ml/ai/ds

Awesome Lists containing this project

README 

        
# Great Papers



- [x] 1976, ["Time Series Forecasting using ARIMA Model"](https://www.sciencedirect.com/science/article/abs/pii/S0304407681000069) by Box and Jenkins

- [x] 1988, ["A Theory of the Learnable"](https://www.jstor.org/stable/20012634) by Valiant

- [x] 1987, ["Pruning Decision Trees" by J. Ross Quinlan](https://link.springer.com/article/10.1007/BF00116251)

- [x] 1990, ["Recurrent Neural Networks" by Elman](https://crl.ucsd.edu/~elman/Papers/fsit.pdf)

- [x] 1995, ["Support Vector Machines" by Corinna Cortes and Vladimir Vapnik](https://link.springer.com/article/10.1023/A:1022627411411)

- [x] 1997, ["Long Short-Term Memory" by Hochreiter and Schmidhuber](https://www.bioinf.jku.at/publications/older/2604.pdf)

- [x] 1998, ["The PageRank Citation Ranking: Bringing Order to the Web" by Sergey Brin and Lawrence Page](http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf)

- [x] 1998, ["Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto](http://incompleteideas.net/book/the-book-2nd.html)

- [x] 1998, ["A Few Useful Things to Know About Naive Bayes" by Andrew McCallum and Kamal Nigam](https://people.cs.umass.edu/~mccallum/papers/naive-bayes-ijcaiws99.pdf)

- [x] 2001, ["The Random Forests Algorithm" by Leo Breiman](https://link.springer.com/article/10.1023/A:1010933404324)

- [x] 2001, ["Statistical Modeling: The Two Cultures"](https://projecteuclid.org/euclid.ss/1009213726) by Breiman

- [x] 2002, ["SMOTE: Synthetic Minority Over-sampling Technique"](https://www.jair.org/index.php/jair/article/view/10302) by Chawla et al.

- [x] 2003, ["A Neural Probabilistic Language Model" by Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin](http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf)

- [ ] 2003, ["LDA: Latent Dirichlet Allocation" by David M. Blei, Andrew Y. Ng, and Michael I. Jordan](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf)

- [x] 2003, ["An Introduction to Variable and Feature Selection" by Isabelle Guyon and Andre Elisseeff](https://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf)

- [x] 2005, ["Gaussian Processes for Machine Learning" by Carl Edward Rasmussen and Christopher K. I. Williams](http://www.gaussianprocess.org/gpml/chapters/RW.pdf)

- [x] 2006, ["A Fast Learning Algorithm for Deep Belief Nets" by Geoffrey Hinton, Simon Osindero, and Yee-Whye Teh](https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf)

- [x] 2007, ["Self-Taught Learning: Transfer Learning from Unlabeled Data" by Raina et al.](https://dl.acm.org/doi/10.1145/1273496.1273592)

- [x] 2007, ["Learning to Rank: From Pairwise Approach to Listwise Approach" by Burges et al.](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR-TR-2010-82.pdf)

- [x] 2009, ["The Unreasonable Effectiveness of Data" by Alon Halevy, Peter Norvig, and Fernando Pereira](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf)

- [x] 2009, ["The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman](https://web.stanford.edu/~hastie/ElemStatLearn/)

- [x] 2010, ["A Few Useful Things to Know About Probability" by John D. Cook](https://www.johndcook.com/Probability_book.pdf)

- [x] 2011, ["Natural Language Processing (Almost) from Scratch" by Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa](https://ronan.collobert.com/pub/matos/2011_nlp_jmlr.pdf)

- [x] 2012, ["Understanding the Bias-Variance Tradeoff" by Scott Fortmann-Roe](http://scott.fortmann-roe.com/docs/BiasVariance.html)

- [x] 2012, ["Practical Recommendations for Gradient-Based Training of Deep Architectures" by Bengio](https://arxiv.org/abs/1206.5533)

- [x] 2012, ["A Few Useful Things to Know About Machine Learning" by Pedro Domingos](https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf)

- [x] 2012, ["Stochastic Gradient Descent Tricks" by Bottou](https://leon.bottou.org/publications/pdf/tricks-2012.pdf)

- [x] 2012, ["Convolutional Neural Networks for Visual Recognition" by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)

- [x] 2013, ["Efficient Estimation of Word Representations in Vector Space" by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean](https://arxiv.org/abs/1301.3781)

- [ ] 2013, ["Distributed Representations of Words and Phrases and their Compositionality" by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean](https://arxiv.org/abs/1310.4546)

- [ ] 2013, ["Variational Autoencoders" by Kingma and Welling](https://arxiv.org/abs/1312.6114)

- [x] 2013, ["Efficient Estimation of Word Representations in Vector Space"](https://arxiv.org/abs/1301.3781) by Mikolov et al.

- [x] 2014, ["Adam: A Method for Stochastic Optimization"](https://arxiv.org/abs/1412.6980) by Kingma and Ba

- [x] 2014, ["Generative Adversarial Nets"](https://arxiv.org/abs/1406.2661) by Goodfellow et al.

- [x] 2014, ["A Tutorial on Principal Component Analysis" by Jonathon Shlens](https://arxiv.org/abs/1404.1100)

- [ ] 2014, ["Generative Modeling by Estimating Gradients of the Data Distribution" by Bengio et al.](https://arxiv.org/abs/1406.1096)

- [ ] 2014, ["Generative Adversarial Networks" by Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio](https://arxiv.org/abs/1406.2661)

- [ ] 2014, ["Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition" by He et al.](https://arxiv.org/abs/1406.4729)

- [ ] 2014, ["Semi-Supervised Learning with Deep Generative Models" by Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, and Max Welling](https://arxiv.org/abs/1406.5298)

- [ ] 2014, ["Neural Machine Translation by Jointly Learning to Align and Translate" by Bahdanau et al.](https://arxiv.org/abs/1409.0473)

- [ ] 2014, ["Generative Adversarial Networks for Conditional Image Synthesis" by Mirza and Osindero](https://arxiv.org/abs/1411.1784)

- [x] 2014, ["Adam: A Method for Stochastic Optimization" by Diederik P. Kingma and Jimmy Ba](https://arxiv.org/abs/1412.6980)

- [x] 2014, ["Dropout: A Simple Way to Prevent Neural Networks from Overfitting" by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov](https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf)

- [ ] 2014, ["DeepFace: Closing the Gap to Human-Level Performance in Face Verification" by Taigman et al.](https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf)

- [ ] 2015, ["DeepDream - a code example for visualizing neural networks" by Mordvintsev et al.](https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html)

- [x] 2015, ["Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" by Sergey Ioffe and Christian Szegedy](https://arxiv.org/abs/1502.03167)

- [ ] 2015, ["Fast R-CNN" by Ross Girshick](https://arxiv.org/abs/1504.08083)

- [ ] 2015, ["U-Net: Convolutional Networks for Biomedical Image Segmentation" by Olaf Ronneberger, Philipp Fischer, and Thomas Brox](https://arxiv.org/abs/1505.04597)

- [ ] 2015, ["Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun](https://arxiv.org/abs/1506.01497)

- [x] 2015, ["Deep Residual Learning for Image Recognition" by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun](https://arxiv.org/abs/1512.03385)

- [x] 2015, ["A Neural Algorithm of Artistic Style"](https://arxiv.org/abs/1508.06576) by Gatys et al.

- [x] 2015, ["Show, Attend and Tell: Neural Image Caption Generation with Visual Attention"](https://arxiv.org/abs/1502.03044) by Xu et al.

- [x] 2016, ["You Only Look Once: Unified, Real-Time Object Detection"](https://arxiv.org/abs/1506.02640) by Redmon et al.

- [x] 2016, ["Deep Neural Networks for YouTube Recommendations"](https://dl.acm.org/doi/10.1145/2959100.2959190) by Covington et al.

- [ ] 2016, ["Neural Architecture Search with Reinforcement Learning" by Zoph and Le](https://arxiv.org/abs/1611.01578)

- [ ] 2016, ["Wavenet: A Generative Model for Raw Audio" by van den Oord et al.](https://arxiv.org/abs/1609.03499)

- [ ] 2017, ["Proximal Policy Optimization Algorithms" by Schulman et al.](https://arxiv.org/abs/1707.06347)

- [x] 2017, ["Attention Is All You Need" by Vaswani et al.](https://arxiv.org/abs/1706.03762)

- [ ] 2017, ["Graph Convolutional Networks" by Kipf and Welling](https://arxiv.org/abs/1609.02907)

- [ ] 2017, ["Dynamic Routing Between Capsules" by Hinton et al.](https://arxiv.org/abs/1710.09829)

- [ ] 2017, ["A Survey of Active Learning" by Settles](https://ieeexplore.ieee.org/document/8233097)

- [x] 2017, ["SHAP: SHapley Additive exPlanations"](https://arxiv.org/abs/1705.07874) by Lundberg and Lee

- [x] 2018, ["Identifying the Context Shift between Test Benchmarks and Production Data"](https://dl.acm.org/doi/10.1145/3287560.3287598) by Sculley et al.

- [x] 2018, ["BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" by Devlin et al.](https://arxiv.org/abs/1810.04805)

- [x] 2018, ["GPT: Improving Language Understanding by Generative Pre-training" by Radford et al.](https://openai.com/research/language-unsupervised)

- [ ] 2018, ["StyleGAN: A Style-Based Generator Architecture for Generative Adversarial Networks" by Karras et al.](https://arxiv.org/abs/1812.04948)

- [ ] 2019, ["XLNet: Generalized Autoregressive Pretraining for Language Understanding" by Yang et al.](https://arxiv.org/abs/1906.08237)

- [ ] 2019, ["Large Scale GAN Training for High Fidelity Natural Image Synthesis" by Brock, Donahue, and Simonyan](https://arxiv.org/abs/1809.11096)

- [x] 2019, ["Language Models Are Few-Shot Learners" by Brown et al.](https://arxiv.org/abs/2005.14165)

- [ ] 2020, ["GPT-3: Language Models are Few-Shot Learners" by Brown et al.](https://arxiv.org/abs/2005.14165)

- [x] 2020, ["DALL·E: Zero-Shot Text-to-Image Generation" by Ramesh et al.](https://arxiv.org/abs/2102.12092)

- [x] 2020, ["Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks"](https://arxiv.org/abs/2005.11401) by Lewis et al.

- [x] 2020, ["Automatic Text Summarization of COVID-19 Medical Research Articles using BERT and GPT-2"](https://ieeexplore.ieee.org/document/9302901) by Al-Shboul et al.

- [x] 2020, ["Data Variability in the Imputation Quality of Missing Data"](https://arxiv.org/abs/2005.01156) by Zhang et al.

- [x] 2020, ["Insider Threat Detection Based on Users’ Mouse Movements and Keystrokes Behavior"](https://ieeexplore.ieee.org/document/9292506) by Ahmed et al.

- [x] 2020, ["An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale"](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al.

- [x] 2021, ["LoRA: Low-Rank Adaptation of Large Language Models"](https://arxiv.org/abs/2106.09685) by Hu et al.

- [ ] 2021, ["CLIP: Learning Transferable Visual Models from Natural Language Supervision" by Radford et al.](https://arxiv.org/abs/2103.00020)

- [ ] 2021, ["PaLM: Scaling Language Modeling with Pathways" by Chowdhery et al.](https://arxiv.org/abs/2204.02311)

- [ ] 2022, ["Diffusion Models Beat GANs on Image Synthesis" by Dhariwal and Nichol](https://arxiv.org/abs/2105.05233)

- [x] 2022, ["Stable Diffusion: High-Resolution Image Synthesis with Latent Diffusion Models" by Rombach et al.](https://arxiv.org/abs/2112.10752)

- [ ] 2022, ["Chinchilla: Training a 70B Chatbot" by Hoffmann et al.](https://arxiv.org/abs/2203.15556)

- [ ] 2022, ["LaMDA: Language Models for Dialog Applications" by Thoppilan et al.](https://arxiv.org/abs/2201.08239)

- [ ] 2022, ["Flamingo: A Visual-Language Model with Multimodal Few-Shot Learning" by Alayrac et al.](https://arxiv.org/abs/2204.14198)

- [x] 2022, ["Unlocking the Potential of Large Language Models for Explainable Recommendations"](https://arxiv.org/abs/2206.08813) by Zhou et al.

- [x] 2023, ["LLaMA: Open and Efficient Foundation Language Models"](https://arxiv.org/abs/2302.13971) by Touvron et al.

- [x] 2023, ["OpenAI GPT-4 Technical Report"](https://arxiv.org/abs/2303.08774)

- [x] 2024, ["Generative AI Security: Challenges and Countermeasures"](https://arxiv.org/abs/2403.01323)