Search Results for author: Roger B. Grosse

Found 9 papers, 2 papers with code

Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks

no code implementations NeurIPS 2019 Guodong Zhang, James Martens, Roger B. Grosse

For two-layer ReLU neural networks (i. e. with one hidden layer), we prove that these two conditions do hold throughout the training under the assumptions that the inputs do not degenerate and the network is over-parameterized.

On the Invertibility of Invertible Neural Networks

no code implementations25 Sep 2019 Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen

Guarantees in deep learning are hard to achieve due to the interplay of flexible modeling schemes and complex tasks.

TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer

2 code implementations ICLR 2019 Sicong Huang, Qiyang Li, Cem Anil, Xuchan Bao, Sageev Oore, Roger B. Grosse

In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness.

Style Transfer

The Reversible Residual Network: Backpropagation Without Storing Activations

8 code implementations NeurIPS 2017 Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse

Deep residual networks (ResNets) have significantly pushed forward the state-of-the-art on image classification, increasing in performance as networks grow both deeper and wider.

General Classification Image Classification

Measuring the reliability of MCMC inference with bidirectional Monte Carlo

no code implementations NeurIPS 2016 Roger B. Grosse, Siddharth Ancha, Daniel M. Roy

Markov chain Monte Carlo (MCMC) is one of the main workhorses of probabilistic inference, but it is notoriously hard to measure the quality of approximate posterior samples.

Probabilistic Programming

Sandwiching the marginal likelihood using bidirectional Monte Carlo

no code implementations8 Nov 2015 Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams

Using the ground truth log-ML estimates obtained from our method, we quantitatively evaluate a wide variety of existing ML estimators on several latent variable models: clustering, a low rank approximation, and a binary attributes model.

Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing

no code implementations30 Dec 2014 Yuri Burda, Roger B. Grosse, Ruslan Salakhutdinov

Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function.

Testing MCMC code

no code implementations16 Dec 2014 Roger B. Grosse, David K. Duvenaud

Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic modeling and inference, but are difficult to debug, and are prone to silent failure if implemented naively.

Annealing between distributions by averaging moments

no code implementations NeurIPS 2013 Roger B. Grosse, Chris J. Maddison, Ruslan R. Salakhutdinov

Many powerful Monte Carlo techniques for estimating partition functions, such as annealed importance sampling (AIS), are based on sampling from a sequence of intermediate distributions which interpolate between a tractable initial distribution and an intractable target distribution.

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