53 citations · 84 across the 4 of their papers we have counts for
7 papers
Variational Generative Stochastic Networks with Collaborative Shaping
Philip Bachman, Doina Precup
We develop an approach to training generative models based on unrolling a variational auto-encoder into a Markov chain, and shaping the chain's trajectories using a technique inspi…
Learning Algorithms for Active Learning
Philip Bachman, Alessandro Sordoni, Adam Trischler
We introduce a model that learns active learning algorithms via metalearning. For a distribution of related tasks, our model jointly learns: a data representation, an item selectio…
Machine Comprehension by Text-to-Text Neural Question Generation
Xingdi Yuan, Tong Wang, Caglar Gulcehre +5
We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervis…
Calibrating Energy-based Generative Adversarial Networks
Zihang Dai, Amjad Almahairi, Philip Bachman +2
In this paper, we propose to equip Generative Adversarial Networks with the ability to produce direct energy estimates for samples.Specifically, we propose a flexible adversarial t…
Natural Language Comprehension with the EpiReader
Adam Trischler, Zheng Ye, Xingdi Yuan +1
We present the EpiReader, a novel model for machine comprehension of text. Machine comprehension of unstructured, real-world text is a major research goal for natural language proc…
A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data
Adam Trischler, Zheng Ye, Xingdi Yuan +3
Understanding unstructured text is a major goal within natural language processing. Comprehension tests pose questions based on short text passages to evaluate such understanding.…