b'R Devon Hjelm'

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

A highly desirable property of a reinforcement learning (RL) agent – and...
06/04/2021 ∙ by Bogdan Mazoure, et al. ∙ 0 ∙ share

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Understanding by Understanding Not: Modeling Negation in Language Models

Negation is a core construction in natural language. Despite being very ...
05/07/2021 ∙ by Arian Hosseini, et al. ∙ 0 ∙ share

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

Zero-shot classification is a generalization task where no instance from...
10/22/2020 ∙ by Tristan Sylvain, et al. ∙ 0 ∙ share

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Implicit Regularization in Deep Learning: A View from Function Space

We approach the problem of implicit regularization in deep learning from...
08/03/2020 ∙ by Aristide Baratin, et al. ∙ 39 ∙ share

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Representation Learning with Video Deep InfoMax

Self-supervised learning has made unsupervised pretraining relevant agai...
07/27/2020 ∙ by R Devon Hjelm, et al. ∙ 0 ∙ share

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Data-Efficient Reinforcement Learning with Momentum Predictive Representations

While deep reinforcement learning excels at solving tasks where large am...
07/12/2020 ∙ by Max Schwarzer, et al. ∙ 0 ∙ share

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Deep Reinforcement and InfoMax Learning

Our work is based on the hypothesis that a model-free agent whose repres...
06/12/2020 ∙ by Bogdan Mazoure, et al. ∙ 0 ∙ share

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Object-Centric Image Generation from Layouts

Despite recent impressive results on single-object and single-domain ima...
03/16/2020 ∙ by Tristan Sylvain, et al. ∙ 7 ∙ share

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

In this contribution, we augment the metric learning setting by introduc...
02/21/2020 ∙ by João Monteiro, et al. ∙ 4 ∙ share

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Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning

Continuous control tasks in reinforcement learning are important because...
09/17/2019 ∙ by Thang Doan, et al. ∙ 15 ∙ share

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Unsupervised State Representation Learning in Atari

State representation learning, or the ability to capture latent generati...
06/19/2019 ∙ by Ankesh Anand, et al. ∙ 4 ∙ share

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Learning Representations by Maximizing Mutual Information Across Views

We propose an approach to self-supervised representation learning based ...
06/03/2019 ∙ by Philip Bachman, et al. ∙ 0 ∙ share

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Batch weight for domain adaptation with mass shift

Unsupervised domain transfer is the task of transferring or translating ...
05/29/2019 ∙ by Mikołaj Bińkowski, et al. ∙ 0 ∙ share

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Leveraging exploration in off-policy algorithms via normalizing flows

Exploration is a crucial component for discovering approximately optimal...
05/16/2019 ∙ by Bogdan Mazoure, et al. ∙ 0 ∙ share

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Prediction of Progression to Alzheimer`s disease with Deep InfoMax

Arguably, unsupervised learning plays a crucial role in the majority of ...
04/24/2019 ∙ by Alex Fedorov, et al. ∙ 0 ∙ share

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Spatio-Temporal Deep Graph Infomax

Spatio-temporal graphs such as traffic networks or gene regulatory syste...
04/12/2019 ∙ by Felix L. Opolka, et al. ∙ 0 ∙ share

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Adversarial Mixup Resynthesizers

In this paper, we explore new approaches to combining information encode...
03/07/2019 ∙ by Christopher Beckham, et al. ∙ 12 ∙ share

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Deep Graph Infomax

We present Deep Graph Infomax (DGI), a general approach for learning nod...
09/27/2018 ∙ by Petar Veličković, et al. ∙ 4 ∙ share

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Learning deep representations by mutual information estimation and maximization

Many popular representation-learning algorithms use training objectives ...
08/20/2018 ∙ by R Devon Hjelm, et al. ∙ 0 ∙ share

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Online Adaptative Curriculum Learning for GANs

Generative Adversarial Networks (GANs) can successfully learn a probabil...
07/31/2018 ∙ by Thang Doan, et al. ∙ 20 ∙ share

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MINE: Mutual Information Neural Estimation

We argue that the estimation of the mutual information between high dime...
01/12/2018 ∙ by Ishmael Belghazi, et al. ∙ 0 ∙ share

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ACtuAL: Actor-Critic Under Adversarial Learning

Generative Adversarial Networks (GANs) are a powerful framework for deep...
11/13/2017 ∙ by Anirudh Goyal, et al. ∙ 0 ∙ share

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Variance Regularizing Adversarial Learning

We introduce a novel approach for training adversarial models by replaci...
07/02/2017 ∙ by Karan Grewal, et al. ∙ 0 ∙ share

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Boundary-Seeking Generative Adversarial Networks

We introduce a novel approach to training generative adversarial network...
02/27/2017 ∙ by R Devon Hjelm, et al. ∙ 0 ∙ share

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Maximum-Likelihood Augmented Discrete Generative Adversarial Networks

Despite the successes in capturing continuous distributions, the applica...
02/26/2017 ∙ by Tong Che, et al. ∙ 0 ∙ share

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

Functional magnetic resonance imaging (fMRI) of temporally-coherent bloo...
11/03/2016 ∙ by R Devon Hjelm, et al. ∙ 0 ∙ share

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Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data

Independent component analysis (ICA), as an approach to the blind source...
03/21/2016 ∙ by R Devon Hjelm, et al. ∙ 0 ∙ share

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Iterative Refinement of Approximate Posterior for Training Directed Belief Networks

Variational methods that rely on a recognition network to approximate th...
11/19/2015 ∙ by R Devon Hjelm, et al. ∙ 0 ∙ share

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R Devon Hjelm

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