Nitish Srivastava
Nitish Srivastava
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
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
225662014
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
53832012
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
T Tieleman, G Hinton
COURSERA: Neural networks for machine learning 4 (2), 26-31, 2012
38112012
Unsupervised learning of video representations using lstms
N Srivastava, E Mansimov, R Salakhudinov
International conference on machine learning, 843-852, 2015
16072015
Multimodal learning with deep boltzmann machines
N Srivastava, RR Salakhutdinov
Advances in neural information processing systems, 2222-2230, 2012
14192012
Neural networks for machine learning lecture 6a overview of mini-batch gradient descent
G Hinton, N Srivastava, K Swersky
Cited on 14 (8), 2012
352*2012
Improving neural networks with dropout
N Srivastava
University of Toronto 182 (566), 7, 2013
2172013
Discriminative transfer learning with tree-based priors
N Srivastava, RR Salakhutdinov
Advances in neural information processing systems, 2094-2102, 2013
1912013
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
1842015
Modeling documents with deep boltzmann machines
N Srivastava, RR Salakhutdinov, GE Hinton
arXiv preprint arXiv:1309.6865, 2013
1832013
Learning representations for multimodal data with deep belief nets
N Srivastava, R Salakhutdinov
International conference on machine learning workshop 79, 2012
1772012
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
1482012
Improving neural networks by preventing co-adaptation of feature detectors (2012)
GE Hinton, N Srivastava, A Krizhevsky, I Sutskever, RR Salakhutdinov
arXiv preprint arXiv:1207.0580, 2012
1072012
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, 0
91
Learning generative models with visual attention
C Tang, N Srivastava, RR Salakhutdinov
Advances in Neural Information Processing Systems, 1808-1816, 2014
762014
Enriching textbooks through data mining
R Agrawal, S Gollapudi, K Kenthapadi, N Srivastava, R Velu
Proceedings of the First ACM Symposium on Computing for Development, 1-9, 2010
542010
Ilya Sutskever, Ruslan, Salakhutdinov
N Srivastava, G Hinton, A Krizhevsky
Dropout: a simple way to prevent neural networks from overfitting, 1929-1958, 1929
441929
System and method for addressing overfitting in a neural network
GE Hinton, A Krizhevsky, I Sutskever, N Srivastva
US Patent 9,406,017, 2016
322016
Initialization strategies of spatio-temporal convolutional neural networks
E Mansimov, N Srivastava, R Salakhutdinov
arXiv preprint arXiv:1503.07274, 2015
322015
T2S-Tensor: Productively generating high-performance spatial hardware for dense tensor computations
N Srivastava, H Rong, P Barua, G Feng, H Cao, Z Zhang, D Albonesi, ...
2019 IEEE 27th Annual International Symposium on Field-Programmable Custom …, 2019
102019
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