Great Papers

• 1976, "Time Series Forecasting using ARIMA Model" by Box and Jenkins
• 1988, "A Theory of the Learnable" by Valiant
• 1987, "Pruning Decision Trees" by J. Ross Quinlan
• 1990, "Recurrent Neural Networks" by Elman
• 1995, "Support Vector Machines" by Corinna Cortes and Vladimir Vapnik
• 1997, "Long Short-Term Memory" by Hochreiter and Schmidhuber
• 1998, "The PageRank Citation Ranking: Bringing Order to the Web" by Sergey Brin and Lawrence Page
• 1998, "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
• 1998, "A Few Useful Things to Know About Naive Bayes" by Andrew McCallum and Kamal Nigam
• 2001, "The Random Forests Algorithm" by Leo Breiman
• 2001, "Statistical Modeling: The Two Cultures" by Breiman
• 2002, "SMOTE: Synthetic Minority Over-sampling Technique" by Chawla et al.
• 2003, "A Neural Probabilistic Language Model" by Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin
• 2003, "LDA: Latent Dirichlet Allocation" by David M. Blei, Andrew Y. Ng, and Michael I. Jordan
• 2003, "An Introduction to Variable and Feature Selection" by Isabelle Guyon and Andre Elisseeff
• 2005, "Gaussian Processes for Machine Learning" by Carl Edward Rasmussen and Christopher K. I. Williams
• 2006, "A Fast Learning Algorithm for Deep Belief Nets" by Geoffrey Hinton, Simon Osindero, and Yee-Whye Teh
• 2007, "Self-Taught Learning: Transfer Learning from Unlabeled Data" by Raina et al.
• 2007, "Learning to Rank: From Pairwise Approach to Listwise Approach" by Burges et al.
• 2009, "The Unreasonable Effectiveness of Data" by Alon Halevy, Peter Norvig, and Fernando Pereira
• 2009, "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
• 2010, "A Few Useful Things to Know About Probability" by John D. Cook
• 2011, "Natural Language Processing (Almost) from Scratch" by Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa
• 2012, "Understanding the Bias-Variance Tradeoff" by Scott Fortmann-Roe
• 2012, "Practical Recommendations for Gradient-Based Training of Deep Architectures" by Bengio
• 2012, "A Few Useful Things to Know About Machine Learning" by Pedro Domingos
• 2012, "Stochastic Gradient Descent Tricks" by Bottou
• 2012, "Convolutional Neural Networks for Visual Recognition" by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton
• 2013, "Efficient Estimation of Word Representations in Vector Space" by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean
• 2013, "Distributed Representations of Words and Phrases and their Compositionality" by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean
• 2013, "Variational Autoencoders" by Kingma and Welling
• 2013, "Efficient Estimation of Word Representations in Vector Space" by Mikolov et al.
• 2014, "Adam: A Method for Stochastic Optimization" by Kingma and Ba
• 2014, "Generative Adversarial Nets" by Goodfellow et al.
• 2014, "A Tutorial on Principal Component Analysis" by Jonathon Shlens
• 2014, "Generative Modeling by Estimating Gradients of the Data Distribution" by Bengio et al.
• 2014, "Generative Adversarial Networks" by Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio
• 2014, "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition" by He et al.
• 2014, "Semi-Supervised Learning with Deep Generative Models" by Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, and Max Welling
• 2014, "Neural Machine Translation by Jointly Learning to Align and Translate" by Bahdanau et al.
• 2014, "Generative Adversarial Networks for Conditional Image Synthesis" by Mirza and Osindero
• 2014, "Adam: A Method for Stochastic Optimization" by Diederik P. Kingma and Jimmy Ba
• 2014, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting" by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov
• 2014, "DeepFace: Closing the Gap to Human-Level Performance in Face Verification" by Taigman et al.
• 2015, "DeepDream - a code example for visualizing neural networks" by Mordvintsev et al.
• 2015, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" by Sergey Ioffe and Christian Szegedy
• 2015, "Fast R-CNN" by Ross Girshick
• 2015, "U-Net: Convolutional Networks for Biomedical Image Segmentation" by Olaf Ronneberger, Philipp Fischer, and Thomas Brox
• 2015, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun
• 2015, "Deep Residual Learning for Image Recognition" by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
• 2015, "A Neural Algorithm of Artistic Style" by Gatys et al.
• 2015, "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention" by Xu et al.
• 2016, "You Only Look Once: Unified, Real-Time Object Detection" by Redmon et al.
• 2016, "Deep Neural Networks for YouTube Recommendations" by Covington et al.
• 2016, "Neural Architecture Search with Reinforcement Learning" by Zoph and Le
• 2016, "Wavenet: A Generative Model for Raw Audio" by van den Oord et al.
• 2017, "Proximal Policy Optimization Algorithms" by Schulman et al.
• 2017, "Attention Is All You Need" by Vaswani et al.
• 2017, "Graph Convolutional Networks" by Kipf and Welling
• 2017, "Dynamic Routing Between Capsules" by Hinton et al.
• 2017, "A Survey of Active Learning" by Settles
• 2017, "SHAP: SHapley Additive exPlanations" by Lundberg and Lee
• 2018, "Identifying the Context Shift between Test Benchmarks and Production Data" by Sculley et al.
• 2018, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" by Devlin et al.
• 2018, "GPT: Improving Language Understanding by Generative Pre-training" by Radford et al.
• 2018, "StyleGAN: A Style-Based Generator Architecture for Generative Adversarial Networks" by Karras et al.
• 2019, "XLNet: Generalized Autoregressive Pretraining for Language Understanding" by Yang et al.
• 2019, "Large Scale GAN Training for High Fidelity Natural Image Synthesis" by Brock, Donahue, and Simonyan
• 2019, "Language Models Are Few-Shot Learners" by Brown et al.
• 2020, "GPT-3: Language Models are Few-Shot Learners" by Brown et al.
• 2020, "DALL·E: Zero-Shot Text-to-Image Generation" by Ramesh et al.
• 2020, "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" by Lewis et al.
• 2020, "Automatic Text Summarization of COVID-19 Medical Research Articles using BERT and GPT-2" by Al-Shboul et al.
• 2020, "Data Variability in the Imputation Quality of Missing Data" by Zhang et al.
• 2020, "Insider Threat Detection Based on Users’ Mouse Movements and Keystrokes Behavior" by Ahmed et al.
• 2020, "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" by Dosovitskiy et al.
• 2021, "LoRA: Low-Rank Adaptation of Large Language Models" by Hu et al.
• 2021, "CLIP: Learning Transferable Visual Models from Natural Language Supervision" by Radford et al.
• 2021, "PaLM: Scaling Language Modeling with Pathways" by Chowdhery et al.
• 2022, "Diffusion Models Beat GANs on Image Synthesis" by Dhariwal and Nichol
• 2022, "Stable Diffusion: High-Resolution Image Synthesis with Latent Diffusion Models" by Rombach et al.
• 2022, "Chinchilla: Training a 70B Chatbot" by Hoffmann et al.
• 2022, "LaMDA: Language Models for Dialog Applications" by Thoppilan et al.
• 2022, "Flamingo: A Visual-Language Model with Multimodal Few-Shot Learning" by Alayrac et al.
• 2022, "Unlocking the Potential of Large Language Models for Explainable Recommendations" by Zhou et al.
• 2023, "LLaMA: Open and Efficient Foundation Language Models" by Touvron et al.
• 2023, "OpenAI GPT-4 Technical Report"
• 2024, "Generative AI Security: Challenges and Countermeasures"