Publications

Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning

Thang Doan

Bogdan Mazoure

Audrey Durand

Joelle Pineau

R Devon Hjelm

Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensiona… (voir plus)l state spaces with deceptive rewards, where the agent can easily become trapped into suboptimal solutions. One way to avoid local optima is to use a population of agents to ensure coverage of the policy space, yet learning a population with the "best" coverage is still an open problem. In this work, we present a novel approach to population-based RL in continuous control that leverages properties of normalizing flows to perform attractive and repulsive operations between current members of the population and previously observed policies. Empirical results on the MuJoCo suite demonstrate a high performance gain for our algorithm compared to prior work, including Soft-Actor Critic (SAC).

2019-09-16

arXiv (Cornell University) (prépublication)

Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks

Santiago Pascual

Mirco Ravanaelli

Joan Parets I Serra

Antonio Bonafonte

Yoshua Bengio

Learning good representations without supervision is still an open issue in machine learning, and is particularly challenging for speech sig… (voir plus)nals, which are often characterized by long sequences with a complex hierarchical structure. Some recent works, however, have shown that it is possible to derive useful speech representations by employing a self-supervised encoder-discriminator approach. This paper proposes an improved self-supervised method, where a single neural encoder is followed by multiple workers that jointly solve different self-supervised tasks. The needed consensus across different tasks naturally imposes meaningful constraints to the encoder, contributing to discover general representations and to minimize the risk of learning superficial ones. Experiments show that the proposed approach can learn transferable, robust, and problem-agnostic features that carry on relevant information from the speech signal, such as speaker identity, phonemes, and even higher-level features such as emotional cues. In addition, a number of design choices make the encoder easily exportable, facilitating its direct usage or adaptation to different problems.

2019-09-14

Interspeech 2019 (publié)

Neural Architecture Search for Class-incremental Learning

Shenyang Huang

Vincent François-Lavet

Guillaume Rabusseau

In class-incremental learning, a model learns continuously from a sequential data stream in which new classes occur. Existing methods often … (voir plus)rely on static architectures that are manually crafted. These methods can be prone to capacity saturation because a neural network's ability to generalize to new concepts is limited by its fixed capacity. To understand how to expand a continual learner, we focus on the neural architecture design problem in the context of class-incremental learning: at each time step, the learner must optimize its performance on all classes observed so far by selecting the most competitive neural architecture. To tackle this problem, we propose Continual Neural Architecture Search (CNAS): an autoML approach that takes advantage of the sequential nature of class-incremental learning to efficiently and adaptively identify strong architectures in a continual learning setting. We employ a task network to perform the classification task and a reinforcement learning agent as the meta-controller for architecture search. In addition, we apply network transformations to transfer weights from previous learning step and to reduce the size of the architecture search space, thus saving a large amount of computational resources. We evaluate CNAS on the CIFAR-100 dataset under varied incremental learning scenarios with limited computational power (1 GPU). Experimental results demonstrate that CNAS outperforms architectures that are optimized for the entire dataset. In addition, CNAS is at least an order of magnitude more efficient than naively using existing autoML methods.

2019-09-13

ArXiv (prépublication)

Torchmeta: A Meta-Learning library for PyTorch

The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research… (voir plus). They offer a way to get a fair comparison between different algorithms, and the wide range of datasets available allows full control over the complexity of this evaluation. However, for a large majority of code available online, the data pipeline is often specific to one dataset, and testing on another dataset requires significant rework. We introduce Torchmeta, a library built on top of PyTorch that enables seamless and consistent evaluation of meta-learning algorithms on multiple datasets, by providing data-loaders for most of the standard benchmarks in few-shot classification and regression, with a new meta-dataset abstraction. It also features some extensions for PyTorch to simplify the development of models compatible with meta-learning algorithms. The code is available here: this https URL

2019-09-13

ArXiv (prépublication)

Learning Speaker Representations with Mutual Information

Mirco Ravanelli

Yoshua Bengio

Learning good representations is of crucial importance in deep learning. Mutual Information (MI) or similar measures of statistical dependen… (voir plus)ce are promising tools for learning these representations in an unsupervised way. Even though the mutual information between two random variables is hard to measure directly in high dimensional spaces, some recent studies have shown that an implicit optimization of MI can be achieved with an encoder-discriminator architecture similar to that of Generative Adversarial Networks (GANs). In this work, we learn representations that capture speaker identities by maximizing the mutual information between the encoded representations of chunks of speech randomly sampled from the same sentence. The proposed encoder relies on the SincNet architecture and transforms raw speech waveform into a compact feature vector. The discriminator is fed by either positive samples (of the joint distribution of encoded chunks) or negative samples (from the product of the marginals) and is trained to separate them. We report experiments showing that this approach effectively learns useful speaker representations, leading to promising results on speaker identification and verification tasks. Our experiments consider both unsupervised and semi-supervised settings and compare the performance achieved with different objective functions.

2019-09-12

Interspeech 2019 (publié)

Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia

Charles C. Onu

Jonathan Lebensold

William L. Hamilton

Doina Precup

Despite continuing medical advances, the rate of newborn morbidity and mortality globally remains high, with over 6 million casualties every… (voir plus) year. The prediction of pathologies affecting newborns based on their cry is thus of significant clinical interest, as it would facilitate the development of accessible, low-cost diagnostic tools\cut{ based on wearables and smartphones}. However, the inadequacy of clinically annotated datasets of infant cries limits progress on this task. This study explores a neural transfer learning approach to developing accurate and robust models for identifying infants that have suffered from perinatal asphyxia. In particular, we explore the hypothesis that representations learned from adult speech could inform and improve performance of models developed on infant speech. Our experiments show that models based on such representation transfer are resilient to different types and degrees of noise, as well as to signal loss in time and frequency domains.

2019-09-12

Interspeech 2019 (publié)

The effect of task and training on intermediate representations in convolutional neural networks revealed with modified RV similarity analysis

Jessica A.F. Thompson

Yoshua Bengio

Marc Schoenwiesner

2019-09-12

2019 Conference on Cognitive Computational Neuroscience (publié)

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Maxime Gasse

Didier Chételat

Nicola Ferroni

Laurent Charlin

Andrea Lodi

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural netw… (voir plus)ork model for learning branch-and-bound variable selection policies, which leverages the natural variable-constraint bipartite graph representation of mixed-integer linear programs. We train our model via imitation learning from the strong branching expert rule, and demonstrate on a series of hard problems that our approach produces policies that improve upon state-of-the-art machine-learning methods for branching and generalize to instances significantly larger than seen during training. Moreover, we improve for the first time over expert-designed branching rules implemented in a state-of-the-art solver on large problems. Code for reproducing all the experiments can be found at this https URL.

2019-09-05

NeurIPS.cc/2019/Reproducibility_Challenge (inconnu)

A Geometric Perspective on Optimal Representations for Reinforcement Learning

Bellemare Marc-Emmanuel

Will Dabney

Robert Dadashi

Adrien Ali Taiga

Pablo Samuel Castro

Nicolas Roux

Dale Schuurmans

Tor Lattimore

Clare Lyle

2019-09-05

NeurIPS.cc/2019/Reproducibility_Challenge (inconnu)

Data-Driven Approach to Encoding and De-Coding 3-D Crystal Structures

Jean Michel Sellier

Generative models have achieved impressive results in many domains including image and text generation. In the natural sciences, generative … (voir plus)models have led to rapid progress in automated drug discovery. Many of the current methods focus on either 1-D or 2-D representations of typically small, drug-like molecules. However, many molecules require 3-D descriptors and exceed the chemical complexity of commonly used dataset. We present a method to encode and decode the position of atoms in 3-D molecules from a dataset of nearly 50,000 stable crystal unit cells that vary from containing 1 to over 100 atoms. We construct a smooth and continuous 3-D density representation of each crystal based on the positions of different atoms. Two different neural networks were trained on a dataset of over 120,000 three-dimensional samples of single and repeating crystal structures, made by rotating the single unit cells. The first, an Encoder-Decoder pair, constructs a compressed latent space representation of each molecule and then decodes this description into an accurate reconstruction of the input. The second network segments the resulting output into atoms and assigns each atom an atomic number. By generating compressed, continuous latent spaces representations of molecules we are able to decode random samples, interpolate between two molecules, and alter known molecules.

2019-09-02

ArXiv (prépublication)

Recognizable series on graphs and hypergraphs

Raphaël Bailly

Guillaume Rabusseau

François Denis

2019-08-31

Journal of Computer and System Sciences (publié)