| Dropout: a simple way to prevent neural networks from overfitting N Srivastava, G Hinton, A Krizhevsky, I Sutskever, R Salakhutdinov The journal of machine learning research 15 (1), 1929-1958, 2014 | 47443 | 2014 |
| Reducing the dimensionality of data with neural networks GE Hinton, RR Salakhutdinov science 313 (5786), 504-507, 2006 | 21645 | 2006 |
| Show, attend and tell: Neural image caption generation with visual attention K Xu, J Ba, R Kiros, K Cho, A Courville, R Salakhutdinov, RS Zemel, ... International Conference on Machine Learning (ICML) 2 (3), 5, 2015 | 11534 | 2015 |
| Improving neural networks by preventing co-adaptation of feature detectors GE Hinton, N Srivastava, A Krizhevsky, I Sutskever, RR Salakhutdinov arXiv preprint arXiv:1207.0580, 2012 | 10724 | 2012 |
| Xlnet: Generalized autoregressive pretraining for language understanding Z Yang, Z Dai, Y Yang, J Carbonell, RR Salakhutdinov, QV Le Advances in neural information processing systems 32, 2019 | 8228 | 2019 |
| Probabilistic matrix factorization R Salakhutdinov, A Mnih Neural Information Processing Systems 21, 2007 | 5221* | 2007 |
| Siamese neural networks for one-shot image recognition G Koch, R Zemel, R Salakhutdinov ICML deep learning workshop 2 (1), 2015 | 4654 | 2015 |
| Transformer-xl: Attentive language models beyond a fixed-length context Z Dai, Z Yang, Y Yang, J Carbonell, QV Le, R Salakhutdinov arXiv preprint arXiv:1901.02860, 2019 | 3455 | 2019 |
| Deep Boltzmann Machines. R Salakhutdinov, GE Hinton International Conference on Artificial Intelligence and Statistics (AISTATS …, 2009 | 3291 | 2009 |
| Human-level concept learning through probabilistic program induction BM Lake, R Salakhutdinov, JB Tenenbaum Science 350 (6266), 1332-1338, 2015 | 3240 | 2015 |
| Unsupervised learning of video representations using lstms N Srivastava, E Mansimov, R Salakhutdinov International Conference on Machine Learning (ICML) 2, 2015 | 2993 | 2015 |
| Skip-thought vectors R Kiros, Y Zhu, RR Salakhutdinov, R Zemel, R Urtasun, A Torralba, ... Advances in neural information processing systems 28, 2015 | 2977 | 2015 |
| Aligning books and movies: Towards story-like visual explanations by watching movies and reading books Y Zhu, R Kiros, R Zemel, R Salakhutdinov, R Urtasun, A Torralba, S Fidler Proceedings of the IEEE international conference on computer vision, 19-27, 2015 | 2706 | 2015 |
| Neighbourhood components analysis J Goldberger, GE Hinton, S Roweis, RR Salakhutdinov Advances in neural information processing systems 17, 2004 | 2698 | 2004 |
| Restricted Boltzmann machines for collaborative filtering R Salakhutdinov, A Mnih, G Hinton Proceedings of the 24th international conference on Machine learning, 791-798, 2007 | 2517 | 2007 |
| Deep sets M Zaheer, S Kottur, S Ravanbakhsh, B Poczos, RR Salakhutdinov, ... Advances in neural information processing systems 30, 2017 | 2373 | 2017 |
| Unifying visual-semantic embeddings with multimodal neural language models R Kiros, R Salakhutdinov, RS Zemel Transactions of the Association for Computational Linguistics (TACL), 2014 | 2230* | 2014 |
| Multimodal learning with deep boltzmann machines N Srivastava, RR Salakhutdinov Advances in neural information processing systems 25, 2012 | 2021 | 2012 |
| 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 | 1991 | 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 | 1991 | 2008 |
Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoVerified email at cs.toronto.edu
