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

Juan Duque

PhD - Université de Montréal

PhD - Université de Montréal

Arian Hosseini

PhD - 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

Ahmed Masry

Collaborating researcher - N/A

PhD - Université de Montréal

Principal supervisor :

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

Collaborating Alumni - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Michell Mercedes Payano Perez

Johan Samir Obando Ceron

PhD - Université de Montréal

Co-supervisor :

Research Intern - Université de Montréal

Dereck Piché

Master's Research - Université de Montréal

pichedereck@gmail.com

Esra'a Saleh

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

Vedant Shah

PhD - Université de Montréal

Master's Research - Université de Montréal

Principal supervisor :

Anna (Cheng-Zhi) Huang

PhD - Université de Montréal

Yusong Wu

PhD - Université de Montréal

Principal supervisor :

Anna (Cheng-Zhi) Huang

sujin yun

PhD - Université de Montréal

Xiaofeng Zhang

PhD - Université de Montréal

Dinghuai Zhang

PhD - Université de Montréal

Co-supervisor :

Hattie Zhou

PhD - Université de Montréal

Principal supervisor :

Hugo Larochelle

Publications

Ordered Memory

Yikang Shen

Shawn Tan

Seyedarian Hosseini

Zhouhan Lin

Alessandro Sordoni

2019-10-29

ArXiv (preprint)

Ordered Memory

Yikang Shen

Shawn Tan

Seyedarian Hosseini

Zhouhan Lin

Alessandro Sordoni

Stack-augmented recurrent neural networks (RNNs) have been of interest to the deep learning community for some time. However, the difficulty… (see more) of training memory models remains a problem obstructing the widespread use of such models. In this paper, we propose the Ordered Memory architecture. Inspired by Ordered Neurons (Shen et al., 2018), we introduce a new attention-based mechanism and use its cumulative probability to control the writing and erasing operation of the memory. We also introduce a new Gated Recursive Cell to compose lower-level representations into higher-level representation. We demonstrate that our model achieves strong performance on the logical inference task (Bowman et al., 2015) and the ListOps (Nangia and Bowman, 2018) task. We can also interpret the model to retrieve the induced tree structure, and find that these induced structures align with the ground truth. Finally, we evaluate our model on the Stanford Sentiment Treebank tasks (Socher et al., 2013), and find that it performs comparatively with the state-of-the-art methods in the literature.

2019-10-29

ArXiv (preprint)

Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery

Shawn Tan

Guillaume Androz

Ahmad Chamseddine

Pierre Fecteau

Joseph Paul Cohen

We release the largest public ECG dataset of continuous raw signals for representation learning containing 11 thousand patients and 2 billio… (see more)n labelled beats. Our goal is to enable semi-supervised ECG models to be made as well as to discover unknown subtypes of arrhythmia and anomalous ECG signal events. To this end, we propose an unsupervised representation learning task, evaluated in a semi-supervised fashion. We provide a set of baselines for different feature extractors that can be built upon. Additionally, we perform qualitative evaluations on results from PCA embeddings, where we identify some clustering of known subtypes indicating the potential for representation learning in arrhythmia sub-type discovery.

2019-10-21

ArXiv (preprint)

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

Kundan Kumar

Rithesh Kumar

Thibault De Boissière

Lucas Gestin

Wei Zhen Teoh

Jose Sotelo

Alexandre De Brébisson

Previous works (Donahue et al., 2018a; Engel et al., 2019a) have found that generating coherent raw audio waveforms with GANs is challenging… (see more). In this paper, we show that it is possible to train GANs reliably to generate high quality coherent waveforms by introducing a set of architectural changes and simple training techniques. Subjective evaluation metric (Mean Opinion Score, or MOS) shows the effectiveness of the proposed approach for high quality mel-spectrogram inversion. To establish the generality of the proposed techniques, we show qualitative results of our model in speech synthesis, music domain translation and unconditional music synthesis. We evaluate the various components of the model through ablation studies and suggest a set of guidelines to design general purpose discriminators and generators for conditional sequence synthesis tasks. Our model is non-autoregressive, fully convolutional, with significantly fewer parameters than competing models and generalizes to unseen speakers for mel-spectrogram inversion. Our pytorch implementation runs at more than 100x faster than realtime on GTX 1080Ti GPU and more than 2x faster than real-time on CPU, without any hardware specific optimization tricks.

2019-10-08

ArXiv (preprint)

Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment

Adrien Ali Taiga

William Fedus

Marlos C. Machado

Marc Gendron-Bellemare

This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We st… (see more)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

2019-08-06

ArXiv (preprint)

Adversarial Computation of Optimal Transport Maps

Jacob Leygonie

Jennifer She*

Amjad Almahairi

Sai Rajeswar

Computing optimal transport maps between high-dimensional and continuous distributions is a challenging problem in optimal transport (OT). G… (see more)enerative adversarial networks (GANs) are powerful generative models which have been successfully applied to learn maps across high-dimensional domains. However, little is known about the nature of the map learned with a GAN objective. To address this problem, we propose a generative adversarial model in which the discriminator's objective is the

2019-06-24

ArXiv (preprint)

Investigating Biases in Textual Entailment Datasets

Shawn Tan

Yikang Shen

Chin-Wei Huang

The ability to understand logical relationships between sentences is an important task in language understanding. To aid in progress for thi… (see more)s task, researchers have collected datasets for machine learning and evaluation of current systems. However, like in the crowdsourced Visual Question Answering (VQA) task, some biases in the data inevitably occur. In our experiments, we find that performing classification on just the hypotheses on the SNLI dataset yields an accuracy of 64%. We analyze the bias extent in the SNLI and the MultiNLI dataset, discuss its implication, and propose a simple method to reduce the biases in the datasets.

2019-06-23

ArXiv (preprint)

Gintare Karolina Dziugaite

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.

2019-06-10

ArXiv (preprint)

Note on the bias and variance of variational inference

Chin-Wei Huang

In this note, we study the relationship between the variational gap and the variance of the (log) likelihood ratio. We show that the gap can… (see more) be upper bounded by some form of dispersion measure of the likelihood ratio, which suggests the bias of variational inference can be reduced by making the distribution of the likelihood ratio more concentrated, such as via averaging and variance reduction.

2019-06-09

ArXiv (preprint)

Representation Mixing for TTS Synthesis

Kyle Kastner

Joao Felipe Santos

Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. Ho… (see more)wever, the choice between character or phoneme input can create serious limitations for practical deployment, as direct control of pronunciation is crucial in certain cases. We demonstrate a simple method for combining multiple types of linguistic information in a single encoder, named representation mixing, enabling flexible choice between character, phoneme, or mixed representations during inference. Experiments and user studies on a public audiobook corpus show the efficacy of our approach.

2019-05-12

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)

doi.org

Brief Report: Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

Yikeng Shen

Shawn Tan

Alessandro Sordoni

Maximum Entropy Generators for Energy-Based Models

Rithesh Kumar

Anirudh Goyal

Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient. In t… (see more)his work, we propose learning both the energy function and an amortized approximate sampling mechanism using a neural generator network, which provides an efficient approximation of the log-likelihood gradient. The resulting objective requires maximizing entropy of the generated samples, which we perform using recently proposed nonparametric mutual information estimators. Finally, to stabilize the resulting adversarial game, we use a zero-centered gradient penalty derived as a necessary condition from the score matching literature. The proposed technique can generate sharp images with Inception and FID scores competitive with recent GAN techniques, does not suffer from mode collapse, and is competitive with state-of-the-art anomaly detection techniques.

2019-01-24

ArXiv (preprint)