David Meger

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McGill University

Detecting GAN generated errors

Despite an impressive performance from the latest GAN for generating hyp...
12/02/2019 ∙ by Xiru Zhu, et al. ∙ 71 ∙ share

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GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects

Mesh models are a promising approach for encoding the structure of 3D ob...
01/31/2019 ∙ by Edward J. Smith, et al. ∙ 18 ∙ share

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Cost Adaptation for Robust Decentralized Swarm Behaviour

The multi-agent swarm system is a robust paradigm which can drive effici...
09/21/2017 ∙ by Peter Henderson, et al. ∙ 0 ∙ share

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Benchmark Environments for Multitask Learning in Continuous Domains

As demand drives systems to generalize to various domains and problems, ...
08/14/2017 ∙ by Peter Henderson, et al. ∙ 0 ∙ share

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Deep Reinforcement Learning that Matters

In recent years, significant progress has been made in solving challengi...
09/19/2017 ∙ by Peter Henderson, et al. ∙ 0 ∙ share

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Improved Adversarial Systems for 3D Object Generation and Reconstruction

This paper describes a new approach for training generative adversarial ...
07/29/2017 ∙ by Edward Smith, et al. ∙ 0 ∙ share

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Semantic Robot Vision Challenge: Current State and Future Directions

The Semantic Robot Vision Competition provided an excellent opportunity ...
08/19/2009 ∙ by Scott Helmer, et al. ∙ 0 ∙ share

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3D Object Super-Resolution

We consider the problem of scaling deep generative shape models to high-...
02/27/2018 ∙ by Edward Smith, et al. ∙ 0 ∙ share

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Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning

We present an algorithm for rapidly learning controllers for robotics sy...
03/06/2018 ∙ by Juan Camilo Gamboa Higuera, et al. ∙ 0 ∙ share

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Off-Policy Deep Reinforcement Learning without Exploration

Reinforcement learning traditionally considers the task of balancing exp...
12/07/2018 ∙ by Scott Fujimoto, et al. ∙ 0 ∙ share

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Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks

Diversity of environments is a key challenge that causes learned robotic...
03/13/2019 ∙ by Sanjay Thakur, et al. ∙ 0 ∙ share

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Human Motion Prediction via Pattern Completion in Latent Representation Space

Inspired by ideas in cognitive science, we propose a novel and general a...
04/18/2019 ∙ by Yi Tian Xu, et al. ∙ 0 ∙ share

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Learning Domain Randomization Distributions for Transfer of Locomotion Policies

Domain randomization (DR) is a successful technique for learning robust ...
06/02/2019 ∙ by Melissa Mozifian, et al. ∙ 0 ∙ share

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Deep learning for Aerosol Forecasting

Reanalysis datasets combining numerical physics models and limited obser...
10/14/2019 ∙ by Caleb Hoyne, et al. ∙ 0 ∙ share

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Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales

Neural Network based controllers hold enormous potential to learn comple...
10/23/2019 ∙ by Sanjay Thakur, et al. ∙ 0 ∙ share

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Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning

Motivated by the recursive Newton-Euler formulation, we propose a novel ...
10/05/2019 ∙ by Sahand Rezaei-Shoshtari, et al. ∙ 0 ∙ share

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David Meger

Featured Co-authors

Joelle Pineau

Doina Precup

Peter Henderson

Adriana Romero

Riashat Islam

Mark Coates

Gregory Dudek

Philip Bachman

Herke van Hoof

Juan Camilo Gamboa Higuera

Scott Fujimoto

Detecting GAN generated errors

GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects

Cost Adaptation for Robust Decentralized Swarm Behaviour

Benchmark Environments for Multitask Learning in Continuous Domains

Deep Reinforcement Learning that Matters

Improved Adversarial Systems for 3D Object Generation and Reconstruction

Semantic Robot Vision Challenge: Current State and Future Directions

3D Object Super-Resolution

Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning

Off-Policy Deep Reinforcement Learning without Exploration

Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks

Human Motion Prediction via Pattern Completion in Latent Representation Space

Learning Domain Randomization Distributions for Transfer of Locomotion Policies

Deep learning for Aerosol Forecasting

Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales

Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning