Aaron Courville

Reza Bayat

PhD - Université de Montréal

Co-supervisor :

Pascal Vincent

Anirudh Buvanesh

PhD - Université de Montréal

Principal supervisor :

Laurent Charlin

anirudb1102@gmail.com

Razvan Ciuca

Master's Research - Université de Montréal

Alexandre Diz Ganito

Master's Research - Université de Montréal

Juan Duque

PhD - Université de Montréal

PhD - Université de Montréal

Arian Hosseini

PhD - Université de Montréal

Uday Kapur

Professional Master's - Université de Montréal

Amr Khalifa

PhD - Université de Montréal

andrei.nicolicioiu@gmail.com

Samuel Lavoie

PhD - Université de Montréal

Zhixuan Lin

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Rishabh Agarwal

Andrei Nicolicioiu

PhD - Université de Montréal

Evgenii Nikishin

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Co-supervisor :

Johan Samir Obando Ceron

PhD - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

pichedereck@gmail.com

Esra'a Saleh

PhD - Université de Montréal

Principal supervisor :

Master's Research - Université de Montréal

Principal supervisor :

Anna (Cheng-Zhi) Huang

Shawn Tan

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

(Rex) Devon Hjelm

Yusong Wu

PhD - Université de Montréal

Principal supervisor :

Anna (Cheng-Zhi) Huang

Xiaofeng Zhang

PhD - Université de Montréal

Dinghuai Zhang

PhD - Université de Montréal

Co-supervisor :

Gintare Karolina Dziugaite

Hattie Zhou

PhD - Université de Montréal

Principal supervisor :

Hugo Larochelle

Publications

Stochastic Neural Network with Kronecker Flow

Chin-Wei Huang

Ahmed Touati

Pascal Vincent

Alexandre Lacoste

Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their capacity to… (see more) scale to the high-dimensional setting of stochastic neural networks. This limitation motivates a need for scalable parameterizations of the noise generation process, in a manner that adequately captures the dependencies among the various parameters. In this work, we address this need and present the Kronecker Flow, a generalization of the Kronecker product to invertible mappings designed for stochastic neural networks. We apply our method to variational Bayesian neural networks on predictive tasks, PAC-Bayes generalization bound estimation, and approximate Thompson sampling in contextual bandits. In all setups, our methods prove to be competitive with existing methods and better than the baselines.

2020-06-03

Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation

Boris Knyazev

Harm de Vries

Cătălina Cangea

Graham W. Taylor

Eugene Belilovsky

2020-05-17

ArXiv (preprint)

Detecting semantic anomalies

Faruk Ahmed

We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmark… (see more)s. While the currently prevalent trend is to consider different datasets as OOD, we argue that out-distributions of practical interest are ones where the distinction is semantic in nature for a specified context, and that evaluative tasks should reflect this more closely. Assuming a context of object recognition, we recommend a set of benchmarks, motivated by practical applications. We make progress on these benchmarks by exploring a multi-task learning based approach, showing that auxiliary objectives for improved semantic awareness result in improved semantic anomaly detection, with accompanying generalization benefits.

2020-04-03

Proceedings of the AAAI Conference on Artificial Intelligence (published)

doi.org

Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images Using a View-Based Representation

Sai Rajeswar

Fahim Mannan

Florian Golemo

Jérôme Parent-Lévesque

David Vazquez

Derek Nowrouzezahrai

2020-03-20

International Journal of Computer Vision (published)

doi.org

Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models

Chin-Wei Huang

Laurent Dinh

Normalizing flows are powerful invertible probabilistic models that can be used to translate two probability distributions, in a way that al… (see more)lows us to efficiently track the change of probability density. However, to trade for computational efficiency in sampling and in evaluating the log-density, special parameterization designs have been proposed at the cost of representational expressiveness. In this work, we propose to use ODEs as a framework to establish universal approximation theory for certain families of flow-based models.

2020-02-26

International Conference on Learning Representations (published)

openreview.net

Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models

Chin-Wei Huang

Laurent Dinh

In this work, we propose a new family of generative flows on an augmented data space, with an aim to improve expressivity without drasticall… (see more)y increasing the computational cost of sampling and evaluation of a lower bound on the likelihood. Theoretically, we prove the proposed flow can approximate a Hamiltonian ODE as a universal transport map. Empirically, we demonstrate state-of-the-art performance on standard benchmarks of flow-based generative modeling.

2020-02-17

ArXiv (preprint)

On Bonus Based Exploration Methods In The Arcade Learning Environment

Adrien Ali Taiga

William Fedus

Marlos C. Machado

Marc Gendron-Bellemare

Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration prob… (see more)lems such as Montezuma's Revenge (Bellemare et al., 2016). Recently, bonus-based exploration methods, which explore by augmenting the environment reward, have reached above-human average performance on such domains. In this paper we reassess popular bonus-based exploration methods within a common evaluation framework. We combine Rainbow (Hessel et al., 2018) with different exploration bonuses and evaluate its performance on Montezuma's Revenge, Bellemare et al.'s set of hard of exploration games with sparse rewards, and the whole Atari 2600 suite. We find that while exploration bonuses lead to higher score on Montezuma's Revenge they do not provide meaningful gains over the simpler epsilon-greedy scheme. In fact, we find that methods that perform best on that game often underperform epsilon-greedy on easy exploration Atari 2600 games. We find that our conclusions remain valid even when hyperparameters are tuned for these easy-exploration games. Finally, we find that none of the methods surveyed benefit from additional training samples (1 billion frames, versus Rainbow's 200 million) on Bellemare et al.'s hard exploration games. Our results suggest that recent gains in Montezuma's Revenge may be better attributed to architecture change, rather than better exploration schemes; and that the real pace of progress in exploration research for Atari 2600 games may have been obfuscated by good results on a single domain.

2020-01-01

ICLR.cc/2020/Conference (poster)

openreview.net

AN ENSEMBLE APPROACH FOR DETECTING MACHINE FAILURE FROM SOUND Technical

Faruk Ahmed

Phong Cao Nguyen

We develop an ensemble-based approach for our submission to the anomaly detection challenge at DCASE 2020. The main members of our ensemble … (see more)are auto-encoders (with reconstruction error as the signal), classiﬁers (with negative predictive conﬁdence as the signal), mismatch of the time-shifted signal with its Fourier-phase-shifted version, and a Gaussian mixture model on a set of common short-term features extracted from the waveform. The scores are passed through an exponential non-linearity and weighted to provide the ﬁnal score, where the weighting and scaling hyper-parameters are learned on the development set. Our ensemble improves over the baseline on the development set.

Learning Classical Planning Transition Functions by Deep Neural Networks

Michaela Urbanovská

Ian G Goodfellow

Université de Montréal Balancing Signals for Semi-Supervised Sequence Learning

Ya Xu

Chris Pal

Training recurrent neural networks (RNNs) on long sequences using backpropagation through time (BPTT) remains a fundamental challenge. It ha… (see more)s been shown that adding a local unsupervised loss term into the optimization objective makes the training of RNNs on long sequences more effective. While the importance of an unsupervised task can in principle be controlled by a coefficient in the objective function, the gradients with respect to the unsupervised loss term still influence all the hidden state dimensions, which might cause important information about the supervised task to be degraded or erased. Compared to existing semi-supervised sequence learning methods, this thesis focuses upon a traditionally overlooked mechanism – an architecture with explicitly designed private and shared hidden units designed to mitigate the detrimental influence of the auxiliary unsupervised loss over the main supervised task. We achieve this by dividing the RNN hidden space into a private space for the supervised task or a shared space for both the supervised and unsupervised tasks. We present extensive experiments with the proposed framework on several long sequence modeling benchmark datasets. Results indicate that the proposed framework can yield performance gains in RNN models where long term dependencies are notoriously challenging to deal with.

Unsupervised Learning of Dense Visual Representations

Pedro O. Pinheiro

Amjad Almahairi

Ryan Y. Benmalek

Florian Golemo

CLOSURE: Assessing Systematic Generalization of CLEVR Models

Dzmitry Bahdanau

Harm de Vries

Timothy O'Donnell

Shikhar Murty

Philippe Beaudoin

2019-12-12

ArXiv (preprint)