259 citations · 976 across the 39 of their papers we have counts for
7 papers · 1 filter
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…
Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks
Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk +4
A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagate credit information backwards through e…
Self-organized Hierarchical Softmax
Yikang Shen, Shawn Tan, Chrisopher Pal +1
We propose a new self-organizing hierarchical softmax formulation for neural-network-based language models over large vocabularies. Instead of using a predefined hierarchical struc…
A step towards procedural terrain generation with GANs
Christopher Beckham, Christopher Pal
Procedural terrain generation for video games has been traditionally been done with smartly designed but handcrafted algorithms that generate heightmaps. We propose a first step to…
Unimodal probability distributions for deep ordinal classification
Christopher Beckham, Christopher Pal
Probability distributions produced by the cross-entropy loss for ordinal classification problems can possess undesired properties. We propose a straightforward technique to constra…
Adversarial Generation of Natural Language
Sai Rajeswar, Sandeep Subramanian, Francis Dutil +2
Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the advers…