| 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 | 52940 | 2014 |
| Improving neural networks by preventing co-adaptation of feature detectors GE Hinton arXiv preprint arXiv:1207.0580, 2012 | 11585 | 2012 |
| Unsupervised learning of video representations using lstms N Srivastava, E Mansimov, R Salakhudinov International conference on machine learning, 843-852, 2015 | 3284 | 2015 |
| Multimodal learning with deep boltzmann machines N Srivastava, RR Salakhutdinov Advances in neural information processing systems 25, 2012 | 2170 | 2012 |
| Neural networks for machine learning lecture 6a overview of mini-batch gradient descent G Hinton, N Srivastava, K Swersky Cited on 14 (8), 2, 2012 | 1630 | 2012 |
| Improving neural networks with dropout N Srivastava University of Toronto 182 (566), 7, 2013 | 417 | 2013 |
| Improving neural networks by preventing co-adaptation of feature detectors. arXiv 2012 GE Hinton, N Srivastava, A Krizhevsky, I Sutskever, RR Salakhutdinov arXiv preprint arXiv:1207.0580, 2012 | 311 | 2012 |
| Discriminative transfer learning with tree-based priors N Srivastava, RR Salakhutdinov Advances in neural information processing systems 26, 2013 | 293 | 2013 |
| Lecture 6a overview of mini–batch gradient descent G Hinton, N Srivastava, K Swersky Coursera Lecture slides https://class. coursera. org/neuralnets-2012-001 …, 2012 | 288 | 2012 |
| Exploiting image-trained CNN architectures for unconstrained video classification S Zha, F Luisier, W Andrews, N Srivastava, R Salakhutdinov arXiv preprint arXiv:1503.04144, 2015 | 278 | 2015 |
| Learning representations for multimodal data with deep belief nets N Srivastava, R Salakhutdinov International conference on machine learning workshop 79 (10.1007), 978-1, 2012 | 254 | 2012 |
| Modeling documents with deep boltzmann machines N Srivastava, RR Salakhutdinov, GE Hinton arXiv preprint arXiv:1309.6865, 2013 | 228 | 2013 |
| Learning generative models with visual attention C Tang, N Srivastava, RR Salakhutdinov Advances in Neural Information Processing Systems 27, 2014 | 186* | 2014 |
| Uncertainty weighted actor-critic for offline reinforcement learning Y Wu, S Zhai, N Srivastava, J Susskind, J Zhang, R Salakhutdinov, H Goh arXiv preprint arXiv:2105.08140, 2021 | 181 | 2021 |
| Salakhutdinov, 2014 Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R H Srivastava Dropout: A simple way to prevent neural networks from overfitting, The …, 2014 | 154 | 2014 |
| Unconstrained scene generation with locally conditioned radiance fields T DeVries, MA Bautista, N Srivastava, GW Taylor, JM Susskind Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 144 | 2021 |
| An attention free transformer S Zhai, W Talbott, N Srivastava, C Huang, H Goh, R Zhang, J Susskind arXiv preprint arXiv:2105.14103, 2021 | 118 | 2021 |
| Capsules with inverted dot-product attention routing YHH Tsai, N Srivastava, H Goh, R Salakhutdinov arXiv preprint arXiv:2002.04764, 2020 | 110 | 2020 |
| System and method for addressing overfitting in a neural network GE Hinton, A Krizhevsky, I Sutskever, N Srivastva US Patent 9,406,017, 2016 | 91 | 2016 |
| Geo rey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from over ing N Srivastava Journal of machine learning research 15 (1), 1929-1958, 2014 | 75 | 2014 |
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
