Learning generative models with visual attention
[paper]
Yichuan Tang, Nitish Srivastava, Ruslan Salakhutdinov
Neural Information Processing Systems (NIPS 2014, oral).
Dropout: A simple way to prevent neural networks from overfitting
[paper][bibtex][code]
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
Journal of Machine Learning Research, June 2014.
Multimodal Learning with Deep Boltzmann Machines
[paper][bibtex]
[Code and results]
Nitish Srivastava and Ruslan Salakhutdinov
Journal of Machine Learning Research, Sept 2014.
Discriminative Transfer Learning with Tree-based Priors
[paper]
[supplementary material]
Nitish Srivastava and Ruslan Salakhutdinov
Neural Information Processing Systems (NIPS 2013).
Modeling Documents with a Deep Boltzmann Machine
[paper]
Nitish Srivastava, Ruslan Salakhutdinov and Geoffrey Hinton
Uncertainty in Artificial Intelligence (UAI 2013, oral).
Multimodal Learning with Deep Boltzmann Machines
[paper]
[supplementary material]
[Code and results]
[poster]
[video]
[bibtex]
Nitish Srivastava and Ruslan R. Salakhutdinov
Neural Information Processing Systems (NIPS 2012, oral).
Earlier version: Multimodal Learning with Deep Belief Nets
[poster]
[paper]
Appeared at the Representation Learning Workshop (ICML 2012).
Enriching textbooks through data mining
Rakesh Agrawal, Sreenivas Gollapudi, Krishnaram Kenthapadi, Nitish Srivastava, Raja Velu
ACM Symposium on Computing for Development (ACM DEV 2010)
[paper]
Unsupervised Learning of Visual Representations using Videos
[pdf][bibtex]
Nitish Srivastava, Depth Oral, May 2015.
Unsupervised Learning of Video Representations using LSTMs
[pdf]
Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov
arXiv preprint, Feb 2015.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
arXiv preprint [paper]
[code]