Roger Grosse
Roger Grosse
Assistant Professor, University of Toronto
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
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
25652009
Importance weighted autoencoders
Y Burda, R Grosse, R Salakhutdinov
arXiv preprint arXiv:1509.00519, 2015
5842015
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
3652011
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
3252013
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
3212009
Optimizing neural networks with kronecker-factored approximate curvature
J Martens, R Grosse
International conference on machine learning, 2408-2417, 2015
3202015
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, 5279-5288, 2017
2902017
Shift-invariant sparse coding for audio classification
R Grosse, R Raina, H Kwong, AY Ng
Uncertainty in AI, 2007
280*2007
Isolating sources of disentanglement in variational autoencoders
RTQ Chen, X Li, RB Grosse, DK Duvenaud
Advances in Neural Information Processing Systems, 2610-2620, 2018
2662018
Automatic Construction and Natural-Language Description of Nonparametric Regression Models.
JR Lloyd, D Duvenaud, RB Grosse, JB Tenenbaum, Z Ghahramani
AAAI, 1242-1250, 2014
1702014
The reversible residual network: Backpropagation without storing activations
AN Gomez, M Ren, R Urtasun, RB Grosse
Advances in neural information processing systems, 2214-2224, 2017
1602017
On the quantitative analysis of decoder-based generative models
Y Wu, Y Burda, R Salakhutdinov, R Grosse
arXiv preprint arXiv:1611.04273, 2016
1582016
A kronecker-factored approximate fisher matrix for convolution layers
R Grosse, J Martens
International Conference on Machine Learning, 573-582, 2016
972016
Exploiting compositionality to explore a large space of model structures
RB Grosse, R Salakhutdinov, WT Freeman, JB Tenenbaum
Uncertainty in AI, 2012
862012
Noisy natural gradient as variational inference
G Zhang, S Sun, D Duvenaud, R Grosse
International Conference on Machine Learning, 5852-5861, 2018
582018
Learning wake-sleep recurrent attention models
J Ba, RR Salakhutdinov, RB Grosse, BJ Frey
Advances in Neural Information Processing Systems, 2593-2601, 2015
522015
Statistical inference, learning and models in big data
B Franke, JF Plante, R Roscher, EA Lee, C Smyth, A Hatefi, F Chen, E Gil, ...
International Statistical Review 84 (3), 371-389, 2016
512016
Scaling up natural gradient by sparsely factorizing the inverse fisher matrix
R Grosse, R Salakhudinov
International Conference on Machine Learning, 2304-2313, 2015
512015
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
502018
Sorting out lipschitz function approximation
C Anil, J Lucas, R Grosse
International Conference on Machine Learning, 291-301, 2019
462019
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