Aaron Courville

Reza Bayat

PhD - Université de Montréal

Co-supervisor :

Pascal Vincent

Anirudh Buvanesh

PhD - Université de Montréal

Principal supervisor :

Laurent Charlin

Razvan Ciuca

Master's Research - Université de Montréal

Juan Duque

PhD - Université de Montréal

PhD - Université de Montréal

Arian Hosseini

PhD - Université de Montréal

Uday Kapur

PhD - Université de Montréal

Amr Khalifa

PhD - Université de Montréal

Samuel Lavoie

PhD - Université de Montréal

Zhixuan Lin

PhD - Université de Montréal

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

Michael Noukhovitch

PhD - Université de Montréal

Johan Samir Obando Ceron

PhD - Université de Montréal

Co-supervisor :

Collaborating researcher - Université de Montréal

Dereck Piché

Master's Research - Université de Montréal

Khaled Rouissi

Master's Research - Université de Montréal

Esra'a Saleh

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

Vedant Shah

PhD - Université de Montréal

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 :

Yoshua Bengio

Publications

Multi-Modal Variational Encoder-Decoders

Iulian V. Serban

Alexander G. Ororbia II

Joelle Pineau

2017-04-23

arXiv.org (preprint)

Char2Wav: End-to-End Speech Synthesis

Jose Sotelo

2017-02-16

International Conference on Learning Representations (unknown)

Deep Nets Don't Learn Via Memorization

David Krueger

Nicolas Ballas

Stanislaw Jastrzebski

Maxinder S. Kanwal

2017-02-16

International Conference on Learning Representations (unknown)

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Iulian V. Serban

Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterance… (see more)s in a dialogue. In an effort to model this kind of generative process, we propose a neural network-based generative architecture, with latent stochastic variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with recent neural network architectures. We evaluate the model performance through automatic evaluation metrics and by carrying out a human evaluation. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate the generation of long outputs and maintain the context.

2017-02-11

Proceedings of the AAAI Conference on Artificial Intelligence (published)

arxiv.org

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation

Iulian V. Serban

Tim Klinger

Gerald Tesauro

Kartik Talamadupula

Bowen Zhou

Yoshua Bengio

We introduce a new class of models called multiresolution recurrent neural networks, which explicitly model natural language generation at m… (see more)ultiple levels of abstraction. The models extend the sequence-to-sequence framework to generate two parallel stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language words (e.g. sentences). The coarse sequences follow a latent stochastic process with a factorial representation, which helps the models generalize to new examples. The coarse sequences can also incorporate task-specific knowledge, when available. In our experiments, the coarse sequences are extracted using automatic procedures, which are designed to capture compositional structure and semantics. These procedures enable training the multiresolution recurrent neural networks by maximizing the exact joint log-likelihood over both sequences. We apply the models to dialogue response generation in the technical support domain and compare them with several competing models. The multiresolution recurrent neural networks outperform competing models by a substantial margin, achieving state-of-the-art results according to both a human evaluation study and automatic evaluation metrics. Furthermore, experiments show the proposed models generate more fluent, relevant and goal-oriented responses.

2017-02-11

Proceedings of the AAAI Conference on Artificial Intelligence (published)

arxiv.org

An Actor-Critic Algorithm for Sequence Prediction

We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL). Curren… (see more)t log-likelihood training methods are limited by the discrepancy between their training and testing modes, as models must generate tokens conditioned on their previous guesses rather than the ground-truth tokens. We address this problem by introducing a \textit{critic} network that is trained to predict the value of an output token, given the policy of an \textit{actor} network. This results in a training procedure that is much closer to the test phase, and allows us to directly optimize for a task-specific score such as BLEU. Crucially, since we leverage these techniques in the supervised learning setting rather than the traditional RL setting, we condition the critic network on the ground-truth output. We show that our method leads to improved performance on both a synthetic task, and for German-English machine translation. Our analysis paves the way for such methods to be applied in natural language generation tasks, such as machine translation, caption generation, and dialogue modelling.

2016-12-31

ICLR.cc/2017/conference (poster)

Adversarially Learned Inference

Vincent Dumoulin

Ishmael Belghazi

Ben Poole

We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an ad… (see more)versarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables. An adversarial game is cast between these two networks and a discriminative network is trained to distinguish between joint latent/data-space samples from the generative network and joint samples from the inference network. We illustrate the ability of the model to learn mutually coherent inference and generation networks through the inspections of model samples and reconstructions and confirm the usefulness of the learned representations by obtaining a performance competitive with state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks.

2016-12-31

ICLR.cc/2017/conference (poster)

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

David Vázquez

Jorge Bernal

F. Javier Sánchez

Gloria Fernández-Esparrach

Antonio M. López

Adriana Romero

Michal Drozdzal

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to… (see more) perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation.

2016-12-31

Journal of Healthcare Engineering (published)

arxiv.org

Calibrating Energy-based Generative Adversarial Networks

Zihang Dai

Amjad Almahairi

Philip Bachman

Eduard Hovy

In this paper, we propose to equip Generative Adversarial Networks with the ability to produce direct energy estimates for samples. Specific… (see more)ally, we propose a flexible adversarial training framework, and prove this framework not only ensures the generator converges to the true data distribution, but also enables the discriminator to retain the density information at the global optimal. We derive the analytic form of the induced solution, and analyze the properties. In order to make the proposed framework trainable in practice, we introduce two effective approximation techniques. Empirically, the experiment results closely match our theoretical analysis, verifying the discriminator is able to recover the energy of data distribution.

2016-12-31

ICLR.cc/2017/conference (poster)

Facilitating Multimodality in Normalizing Flows

Chin-wei Huang

David M. Krueger

The true Bayesian posterior of a model such as a neural network may be highly multimodal. In principle, normalizing flows can represent such… (see more) a distribution via compositions of invertible transformations of random noise. In practice, however, existing normalizing flows may fail to capture most of the modes of a distribution. We argue that the conditionally affine structure of the transformations used in [Dinh et al., 2014, 2016, Kingma et al., 2016] is inefficient, and show that flows which instead use (conditional) invertible non-linear transformations naturally enable multimodality in their output distributions. With just two layers of our proposed deep sigmoidal flow, we are able to model complicated 2d energy functions with much higher fidelity than six layers of deep affine flows.

2016-12-31

(published)

www.semanticscholar.org

Generalizable Features From Unsupervised Learning

Mehdi Mirza

Yoshua Bengio

Humans learn a predictive model of the world and use this model to reason about future events and the consequences of actions. In contrast t… (see more)o most machine predictors, we exhibit an impressive ability to generalize to unseen scenarios and reason intelligently in these settings. One important aspect of this ability is physical intuition(Lake et al., 2016). In this work, we explore the potential of unsupervised learning to find features that promote better generalization to settings outside the supervised training distribution. Our task is predicting the stability of towers of square blocks. We demonstrate that an unsupervised model, trained to predict future frames of a video sequence of stable and unstable block configurations, can yield features that support extrapolating stability prediction to blocks configurations outside the training set distribution

2016-12-31

ICLR.cc/2017/conference (Invite to Workshop Track)