Roger Grosse

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Yuhuai(Tony) WuGoogle / StanfordVerified email at stanford.edu
David DuvenaudAssistant Professor, University of TorontoVerified email at cs.toronto.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Jimmy BaUniversity of TorontoVerified email at cs.toronto.edu
Honglak LeeLG AI Research / U. MichiganVerified email at umich.edu
Rajesh RanganathAssistant Professor, NYUVerified email at cs.princeton.edu
Joshua B. TenenbaumMITVerified email at mit.edu
Yuri BurdaOpenAIVerified email at openai.com
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityVerified email at cam.ac.uk
William T. FreemanProfessor of Computer Science, MITVerified email at mit.edu
Mengye RenNew York University, Google BrainVerified email at nyu.edu
Edward H AdelsonProfessor of Vision Science, MITVerified email at csail.mit.edu
Aidan GomezDepartment of Computer Science, University of OxfordVerified email at cohere.ai
Raquel UrtasunFounder & CEO Waabi, Professor, University of Toronto.Verified email at cs.toronto.edu
Chris J. MaddisonUniversity of TorontoVerified email at cs.toronto.edu
Alexander SchwingUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
Nancy ReidProfessor of Statistics, University of TorontoVerified email at utstat.utoronto.ca
Brendan FreyFounder & CEO, Deep Genomics; Co-Founder, Vector Institute; Professor, University of TorontoVerified email at psi.toronto.edu
Roman GarnettWashington University in St. LouisVerified email at wustl.edu

Roger Grosse

Assistant Professor, University of Toronto

Verified email at cs.toronto.edu - Homepage

Machine learning


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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	3143	2009
Importance weighted autoencoders Y Burda, R Grosse, R Salakhutdinov arXiv preprint arXiv:1509.00519, 2015	963	2015
Isolating sources of disentanglement in variational autoencoders RTQ Chen, X Li, RB Grosse, DK Duvenaud Advances in neural information processing systems 31, 2018	749	2018
Optimizing neural networks with kronecker-factored approximate curvature J Martens, R Grosse International conference on machine learning, 2408-2417, 2015	585	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, 2017	566	2017
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	468	2013
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	449	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	430	2009
The reversible residual network: Backpropagation without storing activations AN Gomez, M Ren, R Urtasun, RB Grosse Advances in neural information processing systems 30, 2017	337	2017
Shift-invariant sparse coding for audio classification R Grosse, R Raina, H Kwong, AY Ng Uncertainty in AI, 2007	318*	2007
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	231	2014
On the quantitative analysis of decoder-based generative models Y Wu, Y Burda, R Salakhutdinov, R Grosse arXiv preprint arXiv:1611.04273, 2016	216	2016
Picking winning tickets before training by preserving gradient flow C Wang, G Zhang, R Grosse arXiv preprint arXiv:2002.07376, 2020	200	2020
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	187	2018
A kronecker-factored approximate fisher matrix for convolution layers R Grosse, J Martens International Conference on Machine Learning, 573-582, 2016	176	2016
Sorting out Lipschitz function approximation C Anil, J Lucas, R Grosse International Conference on Machine Learning, 291-301, 2019	168	2019
Functional variational bayesian neural networks S Sun, G Zhang, J Shi, R Grosse arXiv preprint arXiv:1903.05779, 2019	158	2019
Noisy natural gradient as variational inference G Zhang, S Sun, D Duvenaud, R Grosse International Conference on Machine Learning, 5852-5861, 2018	152	2018
Three mechanisms of weight decay regularization G Zhang, C Wang, B Xu, R Grosse arXiv preprint arXiv:1810.12281, 2018	133	2018
Exploiting compositionality to explore a large space of model structures RB Grosse, R Salakhutdinov, WT Freeman, JB Tenenbaum Uncertainty in AI, 2012	99	2012

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