Publications

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
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
Giancarlo Kerg
Kyle Goyette
Maximilian Puelma Touzel
Gauthier Gidel
Eugene Vorontsov
Yoshua Bengio
Guillaume Lajoie
A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over lo… (voir plus)ng time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary. This ensures eigenvalues with unit norm and thus stable dynamics and training. However this comes at the cost of reduced expressivity due to the limited variety of orthogonal transformations. We propose a novel connectivity structure based on the Schur decomposition and a splitting of the Schur form into normal and non-normal parts. This allows to parametrize matrices with unit-norm eigenspectra without orthogonality constraints on eigenbases. The resulting architecture ensures access to a larger space of spectrally constrained matrices, of which orthogonal matrices are a subset. This crucial difference retains the stability advantages and training speed of orthogonal RNNs while enhancing expressivity, especially on tasks that require computations over ongoing input sequences.
2019-09-05
NeurIPS.cc/2019/Reproducibility_Challenge (inconnu)
doi.org
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
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
Interpolation Consistency Training for Semi-Supervised Learning
Vikas Verma
Kenji Kawaguchi
Alex Lamb
Juho Kannala
Yoshua Bengio
David Lopez-Paz
Arno Solin
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
On the impressive performance of randomly weighted encoders in summarization tasks
Jonathan Pilault
Jaehong Park
Christopher Pal
In this work, we investigate the performance of untrained randomly initialized encoders in a general class of sequence to sequence models an… (voir plus)d compare their performance with that of fully-trained encoders on the task of abstractive summarization. We hypothesize that random projections of an input text have enough representational power to encode the hierarchical structure of sentences and semantics of documents. Using a trained decoder to produce abstractive text summaries, we empirically demonstrate that architectures with untrained randomly initialized encoders perform competitively with respect to the equivalent architectures with fully-trained encoders. We further find that the capacity of the encoder not only improves overall model generalization but also closes the performance gap between untrained randomly initialized and full-trained encoders. To our knowledge, it is the first time that general sequence to sequence models with attention are assessed for trained and randomly projected representations on abstractive summarization.
2019-07-27
ArXiv (prépublication)
arxiv.org
arxiv.org
Combined Reinforcement Learning via Abstract Representations
Vincent François-Lavet
Yoshua Bengio
Doina Precup
Joelle Pineau
In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this p… (voir plus)aper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which opens up new strategies for interpretable AI, exploration and transfer learning.
2019-07-16
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
A Comparative Analysis of Expected and Distributional Reinforcement Learning
Clare Lyle
Pablo Samuel Castro
Bellemare Marc-Emmanuel
Since their introduction a year ago, distributional approaches to reinforcement learning (distributional RL) have produced strong results re… (voir plus)lative to the standard approach which models expected values (expected RL). However, aside from convergence guarantees, there have been few theoretical results investigating the reasons behind the improvements distributional RL provides. In this paper we begin the investigation into this fundamental question by analyzing the differences in the tabular, linear approximation, and non-linear approximation settings. We prove that in many realizations of the tabular and linear approximation settings, distributional RL behaves exactly the same as expected RL. In cases where the two methods behave differently, distributional RL can in fact hurt performance when it does not induce identical behaviour. We then continue with an empirical analysis comparing distributional and expected RL methods in control settings with non-linear approximators to tease apart where the improvements from distributional RL methods are coming from.
2019-07-16
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org