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13 papers · 2 filters
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…
Z-Forcing: Training Stochastic Recurrent Networks
Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté +2
Many efforts have been devoted to training generative latent variable models with autoregressive decoders, such as recurrent neural networks (RNN). Stochastic recurrent models have…
Variational Bi-LSTMs
Samira Shabanian, Devansh Arpit, Adam Trischler +1
Recurrent neural networks like long short-term memory (LSTM) are important architectures for sequential prediction tasks. LSTMs (and RNNs in general) model sequences along the forw…
ACtuAL: Actor-Critic Under Adversarial Learning
Anirudh Goyal, Nan Rosemary Ke, Alex Lamb +4
Generative Adversarial Networks (GANs) are a powerful framework for deep generative modeling. Posed as a two-player minimax problem, GANs are typically trained end-to-end on real-v…
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net
Anirudh Goyal, Nan Rosemary Ke, Surya Ganguli +1
We propose a novel method to directly learn a stochastic transition operator whose repeated application provides generated samples. Traditional undirected graphical models approach…
Fraternal Dropout
Konrad Zolna, Devansh Arpit, Dendi Suhubdy +1
Recurrent neural networks (RNNs) are important class of architectures among neural networks useful for language modeling and sequential prediction. However, optimizing RNNs is know…