Search Results for author: Pablo Sprechmann

Found 17 papers, 6 papers with code

Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning

no code implementations24 Feb 2021 Víctor Campos, Pablo Sprechmann, Steven Hansen, Andre Barreto, Steven Kapturowski, Alex Vitvitskyi, Adrià Puigdomènech Badia, Charles Blundell

We introduce Behavior Transfer (BT), a technique that leverages pre-trained policies for exploration and that is complementary to transferring neural network weights.

reinforcement-learning Unsupervised Pre-training

Temporal Difference Uncertainties as a Signal for Exploration

no code implementations5 Oct 2020 Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L. Borsa, Nicolas Heess, Andre Barreto, Razvan Pascanu

Instead, we incorporate it as an intrinsic reward and treat exploration as a separate learning problem, induced by the agent's temporal difference uncertainties.

Never Give Up: Learning Directed Exploration Strategies

3 code implementations ICLR 2020 Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Bilal Piot, Steven Kapturowski, Olivier Tieleman, Martín Arjovsky, Alexander Pritzel, Andew Bolt, Charles Blundell

Our method doubles the performance of the base agent in all hard exploration in the Atari-57 suite while maintaining a very high score across the remaining games, obtaining a median human normalised score of 1344. 0%.

Atari Games

Fast deep reinforcement learning using online adjustments from the past

1 code implementation NeurIPS 2018 Steven Hansen, Pablo Sprechmann, Alexander Pritzel, André Barreto, Charles Blundell

We propose Ephemeral Value Adjusments (EVA): a means of allowing deep reinforcement learning agents to rapidly adapt to experience in their replay buffer.

Atari Games reinforcement-learning

Meta-Learning by the Baldwin Effect

no code implementations6 Jun 2018 Chrisantha Thomas Fernando, Jakub Sygnowski, Simon Osindero, Jane Wang, Tom Schaul, Denis Teplyashin, Pablo Sprechmann, Alexander Pritzel, Andrei A. Rusu

The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan.

Meta-Learning

Disentangling factors of variation in deep representation using adversarial training

no code implementations NeurIPS 2016 Michael F. Mathieu, Junbo Jake Zhao, Junbo Zhao, Aditya Ramesh, Pablo Sprechmann, Yann Lecun

The only available source of supervision during the training process comes from our ability to distinguish among different observations belonging to the same category.

Disentangling factors of variation in deep representations using adversarial training

3 code implementations10 Nov 2016 Michael Mathieu, Junbo Zhao, Pablo Sprechmann, Aditya Ramesh, Yann Lecun

During training, the only available source of supervision comes from our ability to distinguish among different observations belonging to the same class.

Disentanglement

Accelerating Eulerian Fluid Simulation With Convolutional Networks

1 code implementation ICML 2017 Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin

Efficient simulation of the Navier-Stokes equations for fluid flow is a long standing problem in applied mathematics, for which state-of-the-art methods require large compute resources.

Super-Resolution with Deep Convolutional Sufficient Statistics

1 code implementation18 Nov 2015 Joan Bruna, Pablo Sprechmann, Yann Lecun

Inverse problems in image and audio, and super-resolution in particular, can be seen as high-dimensional structured prediction problems, where the goal is to characterize the conditional distribution of a high-resolution output given its low-resolution corrupted observation.

Bandwidth Extension Image Super-Resolution +1

Audio Source Separation with Discriminative Scattering Networks

no code implementations22 Dec 2014 Pablo Sprechmann, Joan Bruna, Yann Lecun

In this report we describe an ongoing line of research for solving single-channel source separation problems.

Audio Source Separation Frame

Sparse similarity-preserving hashing

no code implementations19 Dec 2013 Jonathan Masci, Alex M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro

In recent years, a lot of attention has been devoted to efficient nearest neighbor search by means of similarity-preserving hashing.

Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching

no code implementations NeurIPS 2013 Marcelo Fiori, Pablo Sprechmann, Joshua Vogelstein, Pablo Musé, Guillermo Sapiro

We also present results on multimodal graphs and applications to collaborative inference of brain connectivity from alignment-free functional magnetic resonance imaging (fMRI) data.

Graph Matching

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