b'R Devon Hjelm'

research

∙ 03/19/2022

The Sandbox Environment for Generalizable Agent Research (SEGAR)

A broad challenge of research on generalization for sequential decision-...

R Devon Hjelm, et al. ∙

research

∙ 01/12/2022

Robust Contrastive Learning against Noisy Views

Contrastive learning relies on an assumption that positive pairs contain...

Ching-Yao Chuang, et al. ∙

research

∙ 06/04/2021

Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL

A highly desirable property of a reinforcement learning (RL) agent – and...

Bogdan Mazoure, et al. ∙

research

∙ 05/07/2021

Understanding by Understanding Not: Modeling Negation in Language Models

Negation is a core construction in natural language. Despite being very ...

Arian Hosseini, et al. ∙

research

∙ 10/22/2020

Zero-Shot Learning from scratch (ZFS): leveraging local compositional representations

Zero-shot classification is a generalization task where no instance from...

Tristan Sylvain, et al. ∙

research

∙ 08/03/2020

Implicit Regularization in Deep Learning: A View from Function Space

We approach the problem of implicit regularization in deep learning from...

Aristide Baratin, et al. ∙

research

∙ 07/27/2020

Representation Learning with Video Deep InfoMax

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

R Devon Hjelm, et al. ∙

research

∙ 07/12/2020

Data-Efficient Reinforcement Learning with Momentum Predictive Representations

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

Max Schwarzer, et al. ∙

research

∙ 06/12/2020

Deep Reinforcement and InfoMax Learning

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

Bogdan Mazoure, et al. ∙

research

∙ 03/16/2020

Object-Centric Image Generation from Layouts

Despite recent impressive results on single-object and single-domain ima...

Tristan Sylvain, et al. ∙

research

∙ 02/21/2020

An end-to-end approach for the verification problem: learning the right distance

In this contribution, we augment the metric learning setting by introduc...

João Monteiro, et al. ∙

research

∙ 09/17/2019

Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning

Continuous control tasks in reinforcement learning are important because...

Thang Doan, et al. ∙

research

∙ 06/19/2019

Unsupervised State Representation Learning in Atari

State representation learning, or the ability to capture latent generati...

Ankesh Anand, et al. ∙

research

∙ 06/03/2019

Learning Representations by Maximizing Mutual Information Across Views

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

Philip Bachman, et al. ∙

research

∙ 05/29/2019

Batch weight for domain adaptation with mass shift

Unsupervised domain transfer is the task of transferring or translating ...

Mikołaj Bińkowski, et al. ∙

research

∙ 05/16/2019

Leveraging exploration in off-policy algorithms via normalizing flows

Exploration is a crucial component for discovering approximately optimal...

Bogdan Mazoure, et al. ∙

research

∙ 04/24/2019

Prediction of Progression to Alzheimer`s disease with Deep InfoMax

Arguably, unsupervised learning plays a crucial role in the majority of ...

Alex Fedorov, et al. ∙

research

∙ 04/12/2019

Spatio-Temporal Deep Graph Infomax

Spatio-temporal graphs such as traffic networks or gene regulatory syste...

Felix L. Opolka, et al. ∙

research

∙ 03/07/2019

Adversarial Mixup Resynthesizers

In this paper, we explore new approaches to combining information encode...

Christopher Beckham, et al. ∙

research

∙ 09/27/2018

Deep Graph Infomax

We present Deep Graph Infomax (DGI), a general approach for learning nod...

Petar Veličković, et al. ∙

research

∙ 08/20/2018

Learning deep representations by mutual information estimation and maximization

Many popular representation-learning algorithms use training objectives ...

R Devon Hjelm, et al. ∙

research

∙ 07/31/2018

Online Adaptative Curriculum Learning for GANs

Generative Adversarial Networks (GANs) can successfully learn a probabil...

Thang Doan, et al. ∙

research

∙ 01/12/2018

MINE: Mutual Information Neural Estimation

We argue that the estimation of the mutual information between high dime...

Ishmael Belghazi, et al. ∙

research

∙ 11/13/2017

ACtuAL: Actor-Critic Under Adversarial Learning

Generative Adversarial Networks (GANs) are a powerful framework for deep...

Anirudh Goyal, et al. ∙

research

∙ 07/02/2017

Variance Regularizing Adversarial Learning

We introduce a novel approach for training adversarial models by replaci...

Karan Grewal, et al. ∙

research

∙ 02/27/2017

Boundary-Seeking Generative Adversarial Networks

We introduce a novel approach to training generative adversarial network...

R Devon Hjelm, et al. ∙

research

∙ 02/26/2017

Maximum-Likelihood Augmented Discrete Generative Adversarial Networks

Despite the successes in capturing continuous distributions, the applica...

Tong Che, et al. ∙

research

∙ 11/03/2016

Recurrent Neural Networks for Spatiotemporal Dynamics of Intrinsic Networks from fMRI Data

Functional magnetic resonance imaging (fMRI) of temporally-coherent bloo...

R Devon Hjelm, et al. ∙

research

∙ 03/21/2016

Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data

Independent component analysis (ICA), as an approach to the blind source...

R Devon Hjelm, et al. ∙

research

∙ 11/19/2015

Iterative Refinement of Approximate Posterior for Training Directed Belief Networks

Variational methods that rely on a recognition network to approximate th...

R Devon Hjelm, et al. ∙

R Devon Hjelm

Featured Co-authors