471 citations · 3.4k across the 65 of their papers we have counts for
5 papers · 1 filter
Sandwiching the marginal likelihood using bidirectional Monte Carlo
Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams
Computing the marginal likelihood (ML) of a model requires marginalizing out all of the parameters and latent variables, a difficult high-dimensional summation or integration probl…
Learning Wake-Sleep Recurrent Attention Models
Jimmy Ba, Roger Grosse, Ruslan Salakhutdinov +1
Despite their success, convolutional neural networks are computationally expensive because they must examine all image locations. Stochastic attention-based models have been shown…
Statistical Inference, Learning and Models in Big Data
Beate Franke, Jean-François Plante, Ribana Roscher +11
The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essenti…
Importance Weighted Autoencoders
Yuri Burda, Roger Grosse, Ruslan Salakhutdinov
The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which a…
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
James Martens, Roger Grosse
We propose an efficient method for approximating natural gradient descent in neural networks which we call Kronecker-Factored Approximate Curvature (K-FAC). K-FAC is based on an ef…