Roger Grosse
Roger Grosse
Assistant Professor, University of Toronto
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Citata da
Citata da
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
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
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
Structure discovery in nonparametric regression through compositional kernel search
D Duvenaud, JR Lloyd, R Grosse, JB Tenenbaum, Z Ghahramani
arXiv preprint arXiv:1302.4922, 2013
Optimizing neural networks with kronecker-factored approximate curvature
J Martens, R Grosse
International conference on machine learning, 2408-2417, 2015
Shift-invariant sparse coding for audio classification
R Grosse, R Raina, H Kwong, AY Ng
Uncertainty in AI, 2007
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
Isolating sources of disentanglement in variational autoencoders
TQ Chen, X Li, RB Grosse, DK Duvenaud
Advances in Neural Information Processing Systems, 2610-2620, 2018
Automatic construction and natural-language description of nonparametric regression models
JR Lloyd, D Duvenaud, R Grosse, J Tenenbaum, Z Ghahramani
Twenty-eighth AAAI conference on artificial intelligence, 2014
On the quantitative analysis of decoder-based generative models
Y Wu, Y Burda, R Salakhutdinov, R Grosse
arXiv preprint arXiv:1611.04273, 2016
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
A kronecker-factored approximate fisher matrix for convolution layers
R Grosse, J Martens
International Conference on Machine Learning, 573-582, 2016
Exploiting compositionality to explore a large space of model structures
RB Grosse, R Salakhutdinov, WT Freeman, JB Tenenbaum
Uncertainty in AI, 2012
Learning wake-sleep recurrent attention models
J Ba, RR Salakhutdinov, RB Grosse, BJ Frey
Advances in Neural Information Processing Systems, 2593-2601, 2015
Noisy natural gradient as variational inference
G Zhang, S Sun, D Duvenaud, R Grosse
arXiv preprint arXiv:1712.02390, 2017
Scaling up natural gradient by sparsely factorizing the inverse fisher matrix
R Grosse, R Salakhudinov
International Conference on Machine Learning, 2304-2313, 2015
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
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
Accurate and conservative estimates of MRF log-likelihood using reverse annealing
Y Burda, R Grosse, R Salakhutdinov
Artificial Intelligence and Statistics, 102-110, 2015
Il sistema al momento non pu eseguire l'operazione. Riprova pi tardi.
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