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.
no code implementations • 25 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.
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.
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.
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.
no code implementations • 8 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.
no code implementations • 30 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.
no code implementations • 16 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.
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.