172 citations · 355 across the 11 of their papers we have counts for
3 papers · 1 filter
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst +24
Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, i…
Inverting Supervised Representations with Autoregressive Neural Density Models
Charlie Nash, Nate Kushman, Christopher K. I. Williams
We present a method for feature interpretation that makes use of recent advances in autoregressive density estimation models to invert model representations. We train generative in…
Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case
Christopher K. I. Williams, Charlie Nash, Alfredo Nazábal
Latent variable models can be used to probabilistically "fill-in" missing data entries. The variational autoencoder architecture (Kingma and Welling, 2014; Rezende et al., 2014) in…