Roger Grosse
Roger Grosse
Assistant Professor, University of Toronto
Verified email at - Homepage
Cited by
Cited by
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
H Lee, R Grosse, R Ranganath, AY Ng
International Conference on Machine Learning, 609-616, 2009
Importance weighted autoencoders
Y Burda, R Grosse, R Salakhutdinov
arXiv preprint arXiv:1509.00519, 2015
Isolating sources of disentanglement in variational autoencoders
RTQ Chen, X Li, R Grosse, D Duvenaud
arXiv preprint arXiv:1802.04942, 2018
Optimizing neural networks with kronecker-factored approximate curvature
J Martens, R Grosse
International conference on machine learning, 2408-2417, 2015
Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation
Y Wu, E Mansimov, RB Grosse, S Liao, J Ba
Advances in neural information processing systems 30, 5279-5288, 2017
Unsupervised learning of hierarchical representations with convolutional deep belief networks
H Lee, R Grosse, R Ranganath, AY Ng
Communications of the ACM 54 (10), 95-103, 2011
Structure discovery in nonparametric regression through compositional kernel search
D Duvenaud, J Lloyd, R Grosse, J Tenenbaum, G Zoubin
International Conference on Machine Learning, 1166-1174, 2013
Ground truth dataset and baseline evaluations for intrinsic image algorithms
R Grosse, MK Johnson, EH Adelson, WT Freeman
International Conference on Computer Vision, 2335-2342, 2009
Shift-invariant sparse coding for audio classification
R Grosse, R Raina, H Kwong, AY Ng
Uncertainty in AI, 2007
The reversible residual network: Backpropagation without storing activations
AN Gomez, M Ren, R Urtasun, RB Grosse
Proceedings of the 31st International Conference on Neural Information …, 2017
Automatic construction and natural-language description of nonparametric regression models
J Lloyd, D Duvenaud, R Grosse, J Tenenbaum, Z Ghahramani
Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014
On the quantitative analysis of decoder-based generative models
Y Wu, Y Burda, R Salakhutdinov, R Grosse
arXiv preprint arXiv:1611.04273, 2016
A kronecker-factored approximate fisher matrix for convolution layers
R Grosse, J Martens
International Conference on Machine Learning, 573-582, 2016
Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Y Wen, P Vicol, J Ba, D Tran, R Grosse
arXiv preprint arXiv:1803.04386, 2018
Noisy natural gradient as variational inference
G Zhang, S Sun, D Duvenaud, R Grosse
International Conference on Machine Learning, 5852-5861, 2018
Sorting out Lipschitz function approximation
C Anil, J Lucas, R Grosse
International Conference on Machine Learning, 291-301, 2019
Functional variational bayesian neural networks
S Sun, G Zhang, J Shi, R Grosse
arXiv preprint arXiv:1903.05779, 2019
Picking winning tickets before training by preserving gradient flow
C Wang, G Zhang, R Grosse
arXiv preprint arXiv:2002.07376, 2020
Three mechanisms of weight decay regularization
G Zhang, C Wang, B Xu, R Grosse
arXiv preprint arXiv:1810.12281, 2018
Exploiting compositionality to explore a large space of model structures
RB Grosse, R Salakhutdinov, WT Freeman, JB Tenenbaum
Uncertainty in AI, 2012
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