Philip Bachman | DeepAI

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∙ 03/10/2023

Ignorance is Bliss: Robust Control via Information Gating

Informational parsimony – i.e., using the minimal information required f...

0 Manan Tomar, et al. ∙

∙ 06/09/2021

Pretraining Representations for Data-Efficient Reinforcement Learning

Data efficiency is a key challenge for deep reinforcement learning. We a...

0 Max Schwarzer, et al. ∙

∙ 07/27/2020

Representation Learning with Video Deep InfoMax

Self-supervised learning has made unsupervised pretraining relevant agai...

0 R Devon Hjelm, et al. ∙

∙ 07/12/2020

Data-Efficient Reinforcement Learning with Momentum Predictive Representations

While deep reinforcement learning excels at solving tasks where large am...

0 Max Schwarzer, et al. ∙

∙ 06/12/2020

Deep Reinforcement and InfoMax Learning

Our work is based on the hypothesis that a model-free agent whose repres...

0 Bogdan Mazoure, et al. ∙

∙ 06/03/2019

Learning Representations by Maximizing Mutual Information Across Views

We propose an approach to self-supervised representation learning based ...

0 Philip Bachman, et al. ∙

∙ 09/07/2018

Learning Invariances for Policy Generalization

While recent progress has spawned very powerful machine learning systems...

0 Remi Tachet des Combes, et al. ∙

∙ 07/11/2018

VFunc: a Deep Generative Model for Functions

We introduce a deep generative model for functions. Our model provides a...

6 Philip Bachman, et al. ∙

∙ 02/27/2018

Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data

Learning inter-domain mappings from unpaired data can improve performanc...

0 Amjad Almahairi, et al. ∙

∙ 09/19/2017

Deep Reinforcement Learning that Matters

In recent years, significant progress has been made in solving challengi...

1 Peter Henderson, et al. ∙

∙ 05/04/2017

Machine Comprehension by Text-to-Text Neural Question Generation

We propose a recurrent neural model that generates natural-language ques...

0 Xingdi Yuan, et al. ∙

∙ 11/29/2016

NewsQA: A Machine Comprehension Dataset

We present NewsQA, a challenging machine comprehension dataset of over 1...

0 Adam Trischler, et al. ∙

∙ 06/11/2016

Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data

Natural language generation plays a critical role in spoken dialogue sys...

0 Shikhar Sharma, et al. ∙

∙ 06/07/2016

Iterative Alternating Neural Attention for Machine Reading

We propose a novel neural attention architecture to tackle machine compr...

0 Alessandro Sordoni, et al. ∙

∙ 06/10/2015

Data Generation as Sequential Decision Making

We connect a broad class of generative models through their shared relia...

0 Philip Bachman, et al. ∙

∙ 12/16/2014

Learning with Pseudo-Ensembles

We formalize the notion of a pseudo-ensemble, a (possibly infinite) coll...

0 Philip Bachman, et al. ∙

∙ 06/27/2012

Improved Estimation in Time Varying Models

Locally adapted parameterizations of a model (such as locally weighted r...

0 Doina Precup, et al. ∙

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