Publications

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)
doi.org
arxiv.org
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)
doi.org
arxiv.org
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)
doi.org
arxiv.org
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)
doi.org
openreview.net
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)
openreview.net
openreview.net
Data-Driven Approach to Encoding and De-Coding 3-D Crystal Structures
Jordan Hoffmann
Louis Maestrati
Yoshihide Sawada
Jian Tang
Jean Michel Sellier
Yoshua Bengio
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)
doi.org
openreview.net
Recognizable series on graphs and hypergraphs
Raphaël Bailly
Guillaume Rabusseau
François Denis
2019-08-31
Journal of Computer and System Sciences (publié)
doi.org
Teaching Modelling Literacy: An Artificial Intelligence Approach
Rijul Saini
Gunter Mussbacher
Jin L.C. Guo
Jörg Kienzle
In Model-Driven Engineering (MDE), models are used to build and analyze complex systems. In the last decades, different modelling formalisms… (voir plus) have been proposed for supporting software development. However, their adoption and practice strongly rely on mastering essential modelling skills to develop a complete and coherent model-based system. Moreover, it is often difficult for novice modellers to get direct and timely feedback and recommendations on their modelling strategies and decisions, particularly in large classroom settings which hinders their learning. Certainly, there is an opportunity to apply Artificial Intelligence (AI) techniques to an MDE learning environment to empower the provisioning of automated and intelligent modelling advocacy. In this paper, we propose a framework called ModBud (a modelling buddy) to educate novice modellers about the art of abstraction. ModBud uses natural language processing (NLP) and machine learning (ML) to create modelling bots with the aim of improving the modelling skills of novice modellers and assisting other practitioners, too. These bots could be used to support teaching with automatic creation or grading of models and enhance learning beyond the traditional classroom-based MDE education with timely feedback and personalized tutoring. Research challenges for the proposed framework are discussed and a research roadmap is presented.
2019-08-31
2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) (publié)
doi.org
Toward Requirements Specification for Machine-Learned Components
Mona Rahimi
Jin L.C. Guo
Sahar Kokaly
Marsha Chechik
In current practice, the behavior of Machine-Learned Components (MLCs) is not sufficiently specified by the predefined requirements. Instead… (voir plus), they "learn" existing patterns from the available training data, and make predictions for unseen data when deployed. On the surface, their ability to extract patterns and to behave accordingly is specifically useful for hard-to-specify concepts in certain safety critical domains (e.g., the definition of a pedestrian in a pedestrian detection component in a vehicle). However, the lack of requirements specifications on their behaviors makes further software engineering tasks challenging for such components. This is especially concerning for tasks such as safety assessment and assurance. In this position paper, we call for more attention from the requirements engineering community on supporting the specification of requirements for MLCs in safety critical domains. Towards that end, we propose an approach to improve the process of requirements specification in which an MLC is developed and operates by explicitly specifying domain-related concepts. Our approach extracts a universally accepted benchmark for hard-to-specify concepts (e.g., "pedestrian") and can be used to identify gaps in the associated dataset and the constructed machine-learned model.
2019-08-31
2019 IEEE 27th International Requirements Engineering Conference Workshops (REW) (publié)
doi.org
Online Continual Learning with Maximally Interfered Retrieval
Rahaf Aljundi
Lucas Caccia
Eugene Belilovsky
Massimo Caccia
Min Lin
Laurent Charlin
Tinne Tuytelaars
Continual learning, the setting where a learning agent is faced with a never ending stream of data, continues to be a great challenge for mo… (voir plus)dern machine learning systems. In particular the online or "single-pass through the data" setting has gained attention recently as a natural setting that is difficult to tackle. Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks. These approaches typically rely on randomly selecting samples from the replay memory or from a generative model, which is suboptimal. In this work, we consider a controlled sampling of memories for replay. We retrieve the samples which are most interfered, i.e. whose prediction will be most negatively impacted by the foreseen parameters update. We show a formulation for this sampling criterion in both the generative replay and the experience replay setting, producing consistent gains in performance and greatly reduced forgetting. We release an implementation of our method at this https URL.
2019-08-10
ArXiv (prépublication)
arxiv.org
arxiv.org
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents
Felipe Petroski Such
Vashisht Madhavan
Rosanne Liu
Rui Wang
Pablo Samuel Castro
Yulun Li
Jiale Zhi
Ludwig Schubert
Bellemare Marc-Emmanuel
Jeff Clune
Joel Lehman
Much human and computational effort has aimed to improve how deep reinforcement learning (DRL) algorithms perform on benchmarks such as the … (voir plus)Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, and investigating and comparing the representations learned by different families of DRL algorithms. Sources of friction include the onerous computational requirements, and general logistical and architectural complications for running DRL algorithms at scale. We lessen this friction, by (1) training several algorithms at scale and releasing trained models, (2) integrating with a previous DRL model release, and (3) releasing code that makes it easy for anyone to load, visualize, and analyze such models. This paper introduces the Atari Zoo framework, which contains models trained across benchmark Atari games, in an easy-to-use format, as well as code that implements common modes of analysis and connects such models to a popular neural network visualization library. Further, to demonstrate the potential of this dataset and software package, we show initial quantitative and qualitative comparisons between the performance and representations of several DRL algorithms, highlighting interesting and previously unknown distinctions between them.
2019-08-09
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment
Adrien Ali Taiga
William Fedus
Marlos C. Machado
Aaron Courville
Bellemare Marc-Emmanuel
This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We st… (voir plus)udy the use of different reward bonuses that incentives exploration in reinforcement learning. We do so by fixing the learning algorithm used and focusing only on the impact of the different exploration bonuses in the agent's performance. We use Rainbow, the state-of-the-art algorithm for value-based agents, and focus on some of the bonuses proposed in the last few years. We consider the impact these algorithms have on performance within the popular game Montezuma's Revenge which has gathered a lot of interest from the exploration community, across the the set of seven games identified by Bellemare et al. (2016) as challenging for exploration, and easier games where exploration is not an issue. We find that, in our setting, recently developed bonuses do not provide significantly improved performance on Montezuma's Revenge or hard exploration games. We also find that existing bonus-based methods may negatively impact performance on games in which exploration is not an issue and may even perform worse than
2019-08-05
ArXiv (prépublication)
arxiv.org
arxiv.org