# Roger B. Grosse

According to our database

Collaborative distances:

^{1}, Roger B. Grosse authored at least 33 papers between 2007 and 2019.Collaborative distances:

## Timeline

#### Legend:

Book In proceedings Article PhD thesis Other## Links

#### Homepages:

#### On csauthors.net:

## Bibliography

2019

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis.

Proceedings of the 36th International Conference on Machine Learning, 2019

Sorting Out Lipschitz Function Approximation.

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Reversible Recurrent Neural Networks.

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Isolating Sources of Disentanglement in Variational Autoencoders.

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Noisy Natural Gradient as Variational Inference.

Proceedings of the 35th International Conference on Machine Learning, 2018

Adversarial Distillation of Bayesian Neural Network Posteriors.

Proceedings of the 35th International Conference on Machine Learning, 2018

Differentiable Compositional Kernel Learning for Gaussian Processes.

Proceedings of the 35th International Conference on Machine Learning, 2018

Understanding Short-Horizon Bias in Stochastic Meta-Optimization.

Proceedings of the 6th International Conference on Learning Representations, 2018

Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches.

Proceedings of the 6th International Conference on Learning Representations, 2018

Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure.

Proceedings of the 6th International Conference on Learning Representations, 2018

Isolating Sources of Disentanglement in Variational Autoencoders.

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

The Reversible Residual Network: Backpropagation Without Storing Activations.

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

On the Quantitative Analysis of Decoder-Based Generative Models.

Proceedings of the 5th International Conference on Learning Representations, 2017

Distributed Second-Order Optimization using Kronecker-Factored Approximations.

Proceedings of the 5th International Conference on Learning Representations, 2017

Discovering and Exploiting Additive Structure for Bayesian Optimization.

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Importance Weighted Autoencoders.

Proceedings of the 4th International Conference on Learning Representations, 2016

Measuring the reliability of MCMC inference with bidirectional Monte Carlo.

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A Kronecker-factored approximate Fisher matrix for convolution layers.

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Learning Wake-Sleep Recurrent Attention Models.

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Optimizing Neural Networks with Kronecker-factored Approximate Curvature.

Proceedings of the 32nd International Conference on Machine Learning, 2015

Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix.

Proceedings of the 32nd International Conference on Machine Learning, 2015

Accurate and conservative estimates of MRF log-likelihood using reverse annealing.

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Model selection in compositional spaces.

PhD thesis, 2014

Model selection in compositional spaces.

PhD thesis, 2014

Automatic Construction and Natural-Language Description of Nonparametric Regression Models.

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

Annealing between distributions by averaging moments.

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Structure Discovery in Nonparametric Regression through Compositional Kernel Search.

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Exploiting compositionality to explore a large space of model structures.

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

2011

Unsupervised learning of hierarchical representations with convolutional deep belief networks.

Commun. ACM, 2011

2009

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Ground truth dataset and baseline evaluations for intrinsic image algorithms.

Proceedings of the IEEE 12th International Conference on Computer Vision, ICCV 2009, Kyoto, Japan, September 27, 2009

2007

Shift-Invariance Sparse Coding for Audio Classification.

Proceedings of the UAI 2007, 2007