7.4k citations · 24.2k across the 136 of their papers we have counts for
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GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
Alex Lamb, Devon Hjelm, Yaroslav Ganin +3
Directed latent variable models that formulate the joint distribution as have the advantage of fast and exact sampling. However, these models have the w…
HoME: a Household Multimodal Environment
Simon Brodeur, Ethan Perez, Ankesh Anand +6
We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all with…
Neural Language Modeling by Jointly Learning Syntax and Lexicon
Yikang Shen, Zhouhan Lin, Chin-Wei Huang +1
We propose a neural language model capable of unsupervised syntactic structure induction. The model leverages the structure information to form better semantic representations and…
Learnable Explicit Density for Continuous Latent Space and Variational Inference
Chin-Wei Huang, Ahmed Touati, Laurent Dinh +4
In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its corresponding posterior. First, we decompose the…
Bayesian Hypernetworks
David Krueger, Chin-Wei Huang, Riashat Islam +3
We study Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork $\h$ is a neural network which learns to transform a sim…
FiLM: Visual Reasoning with a General Conditioning Layer
Ethan Perez, Florian Strub, Harm de Vries +2
We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, f…