Guillaume Desjardins | DeepAI

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∙ 08/24/2023

APART: Diverse Skill Discovery using All Pairs with Ascending Reward and DropouT

We study diverse skill discovery in reward-free environments, aiming to ...

0 Hadar Schreiber Galler, et al. ∙

∙ 06/01/2021

Reward is enough for convex MDPs

Maximising a cumulative reward function that is Markov and stationary, i...

0 Tom Zahavy, et al. ∙

∙ 10/27/2020

Behavior Priors for Efficient Reinforcement Learning

As we deploy reinforcement learning agents to solve increasingly challen...

10 Dhruva Tirumala, et al. ∙

∙ 09/10/2020

Importance Weighted Policy Learning and Adaption

The ability to exploit prior experience to solve novel problems rapidly ...

6 Alexandre Galashov, et al. ∙

∙ 05/03/2019

Information asymmetry in KL-regularized RL

Many real world tasks exhibit rich structure that is repeated across dif...

6 Alexandre Galashov, et al. ∙

∙ 04/10/2018

Understanding disentangling in β-VAE

We present new intuitions and theoretical assessments of the emergence o...

0 Christopher P Burgess, et al. ∙

∙ 12/02/2016

Overcoming catastrophic forgetting in neural networks

The ability to learn tasks in a sequential fashion is crucial to the dev...

0 James Kirkpatrick, et al. ∙

∙ 05/09/2016

Theano: A Python framework for fast computation of mathematical expressions

Theano is a Python library that allows to define, optimize, and evaluate...

0 The Theano Development Team, et al. ∙

∙ 07/01/2015

Natural Neural Networks

We introduce Natural Neural Networks, a novel family of algorithms that ...

0 Guillaume Desjardins, et al. ∙

∙ 11/19/2014

Compress and Control

This paper describes a new information-theoretic policy evaluation techn...

0 Joel Veness, et al. ∙

∙ 10/01/2014

Deep Tempering

Restricted Boltzmann Machines (RBMs) are one of the fundamental building...

0 Guillaume Desjardins, et al. ∙

∙ 01/16/2013

Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines

This paper introduces the Metric-Free Natural Gradient (MFNG) algorithm ...

0 Guillaume Desjardins, et al. ∙

∙ 10/19/2012

Disentangling Factors of Variation via Generative Entangling

Here we propose a novel model family with the objective of learning to d...

0 Guillaume Desjardins, et al. ∙

∙ 03/20/2012

On Training Deep Boltzmann Machines

The deep Boltzmann machine (DBM) has been an important development in th...

0 Guillaume Desjardins, et al. ∙

∙ 12/15/2010

Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs

Restricted Boltzmann Machines (RBM) have attracted a lot of attention of...

0 Guillaume Desjardins, et al. ∙

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