Sandwiching the marginal likelihood using bidirectional Monte Carlo
. Roger Grosse, Zoubin Ghahramani, and Ryan Adams, 2015.
Discovering and exploiting additive structure for Bayesian optimization. Jacob Gardner, Chuan Guo, Kilian Weinberger, Roman Garnett, and Roger Grosse, AISTATS 2017. (coming soon)
Distributed second-order optimization using Kronecker-factored approximations
. Jimmy Ba, Roger Grosse, and James Martens, ICLR 2017.
On the quantitative analysis of decoder-based generative models.
. Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger Grosse, ICLR 2017.
Measuring the reliability of MCMC inference with bidirectional Monte Carlo
. Roger Grosse, Siddharth Ancha, and Daniel Roy, NIPS 2016.
A Kronecker-factored approximate Fisher matrix for convolution layers
. Roger Grosse and James Martens, ICML 2016.
Importance weighted autoencoders
. Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov. ICLR 2016.
Learning wake-sleep recurrent attention models
. Jimmy Ba, Roger Grosse, Ruslan Salakhutdinov, and Brendan Frey, NIPS 2015.
Scaling up natural gradient by sparsely factorizing the inverse Fisher matrix
. Roger Grosse and Ruslan Salakhutdinov, ICML 2015.
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
. James Martens and Roger Grosse, ICML 2015.
(terser and less readable than the arXiv version)
Accurate and conservative estimates of MRF log-likelihood using reverse annealing
. Yuri Burda, Roger B. Grosse, and Ruslan Salakhutdinov, AISTATS 2015.
Automatic construction and natural-language description of nonparametric regression models
. James R. Lloyd, David Duvenaud, Roger B. Grosse, Joshua B. Tenenbaum, and Zoubin Ghahramani. AAAI 2014.
Annealing between distributions by averaging moments
. Roger B. Grosse, Chris J. Maddison, and Ruslan Salakhutdinov. NIPS 2013.
(from the ICML 2013 workshop
Challenges in Representation Learning
Structure discovery in nonparametric regression through compositional kernel search
. David Duvenaud, James R. Lloyd, Roger B. Grosse, Joshua B. Tenenbaum, and Zoubin Ghahramani. ICML 2013.
Exploiting compositionality to explore a large space of model structures
. Roger B. Grosse, Ruslan Salakhutdinov, William T. Freeman, and Joshua B. Tenenbaum, UAI 2012.
Best Student Paper.
Unsupervised learning of hierarchical representations with convolutional deep belief networks
. Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng,
Communications of the ACM
, vol. 54, no. 10, pp. 95-103, 2011.
A ground-truth dataset and baseline evaluations for intrinsic image algorithms
. Roger Grosse, Micah K. Johnson, Edward Adelson, and William T. Freeman, ICCV 2009.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
. Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng, ICML 2009.
Best Application Paper
Shift-invariant sparse coding for audio classification
. Roger Grosse, Rajat Raina, Helen Kwong, and Andrew Y. Ng, UAI 2007
Model selection in compositional spaces
. Ph.D. thesis, 2014.