| Probabilistic matrix factorization R Salakhutdinov, A Mnih Advances in neural information processing systems 20, 1257-1264, 2008 | 3942* | 2008 |
| Restricted Boltzmann machines for collaborative filtering R Salakhutdinov, A Mnih, G Hinton Proceedings of the 24th international conference on Machine learning, 791-798, 2007 | 1967 | 2007 |
| Bayesian probabilistic matrix factorization using Markov chain Monte Carlo R Salakhutdinov, A Mnih Proceedings of the 25th international conference on Machine learning, 880-887, 2008 | 1476 | 2008 |
| Bayesian probabilistic matrix factorization using Markov chain Monte Carlo R Salakhutdinov, A Mnih Proceedings of the 25th international conference on Machine learning, 880-887, 2008 | 1461 | 2008 |
| The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables CJ Maddison, A Mnih, YW Teh International Conference on Learning Representations 2017, 2016 | 1120 | 2016 |
| A scalable hierarchical distributed language model A Mnih, GE Hinton Advances in Neural Information Processing Systems 21, 1081-1088, 2009 | 1083 | 2009 |
| Three new graphical models for statistical language modelling A Mnih, G Hinton Proceedings of the 24th international conference on Machine learning, 641-648, 2007 | 678 | 2007 |
| Neural Variational Inference and Learning in Belief Networks A Mnih, K Gregor International Conference on Machine Learning 2014, 2014 | 600 | 2014 |
| Learning word embeddings efficiently with noise-contrastive estimation A Mnih, K Kavukcuoglu Advances in neural information processing systems 26, 2265-2273, 2013 | 539 | 2013 |
| A fast and simple algorithm for training neural probabilistic language models A Mnih, YW Teh International Conference on Machine Learning 2012, 2012 | 536 | 2012 |
| Disentangling by factorising H Kim, A Mnih International Conference on Machine Learning 2018, 2018 | 448 | 2018 |
| Deep autoregressive networks K Gregor, I Danihelka, A Mnih, C Blundell, D Wierstra Proceedings of the 31st International Conference on Machine Learning (ICML …, 2014 | 226 | 2014 |
| Variational inference for Monte Carlo objectives A Mnih, DJ Rezende International Conference on Machine Learning 2016, 2016 | 216 | 2016 |
| REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models G Tucker, A Mnih, CJ Maddison, J Lawson, J Sohl-Dickstein Advances in Neural Information Processing Systems, 2624-2633, 2017 | 191 | 2017 |
| Filtering Variational Objectives CJ Maddison, D Lawson, G Tucker, N Heess, M Norouzi, A Mnih, ... Advances in Neural Information Processing Systems 2017, 2017 | 117 | 2017 |
| MuProp: Unbiased Backpropagation for Stochastic Neural Networks S Gu, S Levine, I Sutskever, A Mnih International Conference on Learning Representations 2016, 2015 | 116 | 2015 |
| Implicit reparameterization gradients M Figurnov, S Mohamed, A Mnih Advances in Neural Information Processing Systems 31, 441-452, 2018 | 114 | 2018 |
| Attentive Neural Processes H Kim, A Mnih, J Schwarz, M Garnelo, A Eslami, D Rosenbaum, O Vinyals, ... International Conference on Learning Representations 2019, 2018 | 105 | 2018 |
| Visualizing similarity data with a mixture of maps J Cook, I Sutskever, A Mnih, G Hinton Artificial Intelligence and Statistics, 67-74, 2007 | 102 | 2007 |
| Monte Carlo Gradient Estimation in Machine Learning S Mohamed, M Rosca, M Figurnov, A Mnih Journal of Machine Learning Research 21 (132), 1-62, 2020 | 75 | 2020 |
Geoffrey HintonEmeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, GoogleVerified email at cs.toronto.edu
