David Warde-Farley | DeepAI

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∙ 07/29/2021

Learning more skills through optimistic exploration

Unsupervised skill learning objectives (Gregor et al., 2016, Eysenbach e...

7 DJ Strouse, et al. ∙

∙ 12/14/2020

Relative Variational Intrinsic Control

In the absence of external rewards, agents can still learn useful behavi...

0 Kate Baumli, et al. ∙

∙ 01/22/2020

Q-Learning in enormous action spaces via amortized approximate maximization

Applying Q-learning to high-dimensional or continuous action spaces can ...

0 Tom Van de Wiele, et al. ∙

∙ 06/12/2019

Fast Task Inference with Variational Intrinsic Successor Features

It has been established that diverse behaviors spanning the controllable...

0 Steven Hansen, et al. ∙

∙ 11/28/2018

Unsupervised Control Through Non-Parametric Discriminative Rewards

Learning to control an environment without hand-crafted rewards or exper...

0 David Warde-Farley, et al. ∙

∙ 06/15/2017

Variational Approaches for Auto-Encoding Generative Adversarial Networks

Auto-encoding generative adversarial networks (GANs) combine the standar...

0 Mihaela Rosca, 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. ∙

∙ 06/01/2015

Blocks and Fuel: Frameworks for deep learning

We introduce two Python frameworks to train neural networks on large dat...

0 Bart van Merriënboer, et al. ∙

∙ 05/13/2015

Brain Tumor Segmentation with Deep Neural Networks

In this paper, we present a fully automatic brain tumor segmentation met...

0 Mohammad Havaei, et al. ∙

∙ 03/05/2015

EmoNets: Multimodal deep learning approaches for emotion recognition in video

The task of the emotion recognition in the wild (EmotiW) Challenge is to...

0 Samira Ebrahimi Kahou, et al. ∙

∙ 12/20/2014

Self-informed neural network structure learning

We study the problem of large scale, multi-label visual recognition with...

0 David Warde-Farley, et al. ∙

∙ 06/10/2014

Generative Adversarial Networks

We propose a new framework for estimating generative models via an adver...

0 Ian J. Goodfellow, et al. ∙

∙ 12/21/2013

An empirical analysis of dropout in piecewise linear networks

The recently introduced dropout training criterion for neural networks h...

0 David Warde-Farley, et al. ∙

∙ 08/20/2013

Pylearn2: a machine learning research library

Pylearn2 is a machine learning research library. This does not just mean...

0 Ian J. Goodfellow, et al. ∙

∙ 02/18/2013

Maxout Networks

We consider the problem of designing models to leverage a recently intro...

0 Ian J. Goodfellow, et al. ∙

∙ 11/23/2012

Theano: new features and speed improvements

Theano is a linear algebra compiler that optimizes a user's symbolically...

0 Frédéric Bastien, et al. ∙

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