- 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
- 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
- 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, "Spatial Transformer Networks" by Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu
- 2015, "YOLO: You Only Look Once - Unified, Real-Time Object Detection" by Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi
- 2015, "Neural Style Transfer" by Gatys et al.
- 2015, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks" by Radford et al.
- 2015, "Deep Learning" by Yann LeCun, Yoshua Bengio, and Geoffrey Hinton
- 2016, "Deep Residual Learning for Image Recognition" by He et al.
- 2016, "XGBoost: A Scalable Tree Boosting System" by Tianqi Chen and Carlos Guestrin
- 2016, "One-shot Learning with Memory-Augmented Neural Networks" by Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap
- 2016, "Wide & Deep Learning for Recommender Systems" by Cheng et al.
- 2016, "Graph Convolutional Networks" by Thomas N. Kipf and Max Welling
- 2016, "WaveNet: A Generative Model for Raw Audio" by Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu
- 2017, "Generative Models for Effective ML on Private Data" by Brendan McMahan and Galen Andrew
- 2017, "DenseNet: Densely Connected Convolutional Networks" by Huang et al.
- 2017, "Neural Architecture Search with Reinforcement Learning" by Barret Zoph and Quoc V. Le
- 2017, "Generative Adversarial Networks for Image-to-Image Translation" by Isola et al.
- 2017, "Generative Adversarial Networks with Wasserstein Distance" by Arjovsky et al.
- 2017, "Generative Adversarial Networks for Text Generation" by Zhang et al.
- 2017, "Mask R-CNN" by Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick
- 2017, "CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks" by Zhu et al.
- 2017, "Attention Is All You Need" by Vaswani et al.
- 2017, "Capsule Networks" by Sabour et al.
- 2017, "Deep Reinforcement Learning" by Li et al.
- 2018, "Neural Ordinary Differential Equations" by Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K. Duvenaud
- 2018, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" by Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova
- 2018, "A Style-Based Generator Architecture for Generative Adversarial Networks" by Karras et al.
- 2019, "Generative Adversarial Networks: An Overview" by Goodfellow et al.
- 2019, "Generative Adversarial Networks for Speech Enhancement" by Donahue et al.
- 2019, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" by Tan and Le
- 2021, "EfficientNetV2: Smaller Models and Faster Training" by Tan et al.