Aaron Courville

sarvjeet-singh.ghotra@mila.quebec

Juan Duque

PhD - Université de Montréal

juan.duque@mila.quebec

PhD - Université de Montréal

udaykaran.kapur@mila.quebec

Arian Hosseini

PhD - Université de Montréal

Uday Kapur

PhD - Université de Montréal

Amr Khalifa

PhD - Université de Montréal

amr-khalifa.alshaykh@mila.quebec

samuel.lavoie@mila.quebec

Samuel Lavoie

PhD - Université de Montréal

Zhixuan Lin

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

Simon Lacoste-Julien

alan.milligan@mila.quebec

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

andjela.mladenovic@mila.quebec

mohammed.muqeeth@mila.quebec

Muqeeth Mohammed

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Rishabh Agarwal

morgane.moss@mila.quebec

Andrei Nicolicioiu

PhD - Université de Montréal

andrei.nicolicioiu@gmail.com

michell.payanoperez@mila.quebec

Michael Noukhovitch

PhD - Université de Montréal

Johan Samir Obando Ceron

PhD - Université de Montréal

Co-supervisor :

Pablo Samuel Castro

johan.ceron@mila.quebec

PhD - Université de Montréal

Dereck Piché

PhD - Université de Montréal

pichedereck@gmail.com

khaled.rouissi@mila.quebec

Khaled Rouissi

Master's Research - Université de Montréal

Esra'a Saleh

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

esraa.saleh@mila.quebec

Vedant Shah

PhD - Université de Montréal

vedant.shah@mila.quebec

PhD - Université de Montréal

tsirigoc@mila.quebec

colin.yiyang-lu@mila.quebec

Yusong Wu

PhD - Université de Montréal

Principal supervisor :

Anna (Cheng-Zhi) Huang

wu.yusong@mila.quebec

Colin Yiyang Lu

PhD - Université de Montréal

Sujin yun

PhD - Université de Montréal

sujin.yun@mila.quebec

xiaofeng.zhang@mila.quebec

Xiaofeng Zhang

PhD - Université de Montréal

Publications

Piecewise Latent Variables for Neural Variational Text Processing

Iulian V. Serban

Alexander G. Ororbia II

Joelle Pineau

Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variable… (see more)s, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.

2017-08-31

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (published)

End-to-end optimization of goal-driven and visually grounded dialogue systems

Florian Strub

Harm de Vries

Jérémie Mary

Bilal Piot

Olivier Pietquin

End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architec… (see more)tures for sequence-to-sequence learning. Yet, most current approaches cast human-machine dialogue management as a supervised learning problem, aiming at predicting the next utterance of a participant given the full history of the dialogue. This vision is too simplistic to render the intrinsic planning problem inherent to dialogue as well as its grounded nature , making the context of a dialogue larger than the sole history. This is why only chitchat and question answering tasks have been addressed so far using end-to-end architectures. In this paper, we introduce a Deep Reinforcement Learning method to optimize visually grounded task-oriented dialogues , based on the policy gradient algorithm. This approach is tested on a dataset of 120k dialogues collected through Mechanical Turk and provides encouraging results at solving both the problem of generating natural dialogues and the task of discovering a specific object in a complex picture.

2017-08-18

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (published)

A Closer Look at Memorization in Deep Networks

Devansh Arpit

Stanisław Jastrzębski

David Krueger

Maxinder S. Kanwal

We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While dee… (see more)p networks are capable of memorizing noise data, our results suggest that they tend to prioritize learning simple patterns first. In our experiments, we expose qualitative differences in gradient-based optimization of deep neural networks (DNNs) on noise vs. real data. We also demonstrate that for appropriately tuned explicit regularization (e.g., dropout) we can degrade DNN training performance on noise datasets without compromising generalization on real data. Our analysis suggests that the notions of effective capacity which are dataset independent are unlikely to explain the generalization performance of deep networks when trained with gradient based methods because training data itself plays an important role in determining the degree of memorization.

2017-08-05

International Conference on Machine Learning (unknown)

proceedings.mlr.press

Adversarial Generation of Natural Language

Sandeep Subramanian

Sai Rajeswar

Francis Dutil

Christopher Pal

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for … (see more)image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.

2017-07-31

Proceedings of the 2nd Workshop on Representation Learning for NLP (published)

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.

2017-07-25

Journal of Healthcare Engineering (published)

Self-organized Hierarchical Softmax

Yikang Shen

Shawn Tan

Christopher Pal

We propose a new self-organizing hierarchical softmax formulation for neural-network-based language models over large vocabularies. Instead … (see more)of using a predefined hierarchical structure, our approach is capable of learning word clusters with clear syntactical and semantic meaning during the language model training process. We provide experiments on standard benchmarks for language modeling and sentence compression tasks. We find that this approach is as fast as other efficient softmax approximations, while achieving comparable or even better performance relative to similar full softmax models.

2017-07-25

ArXiv (preprint)

A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering

Tegan Maharaj

Nicolas Ballas

Anna Rohrbach

Christopher Pal

While deep convolutional neural networks frequently approach or exceed human-level performance at benchmark tasks involving static images, e… (see more)xtending this success to moving images is not straightforward. Having models which can learn to understand video is of interest for many applications, including content recommendation, prediction, summarization, event/object detection and understanding human visual perception, but many domains lack sufficient data to explore and perfect video models. In order to address the need for a simple, quantitative benchmark for developing and understanding video, we present MovieFIB, a fill-in-the-blank question-answering dataset with over 300,000 examples, based on descriptive video annotations for the visually impaired. In addition to presenting statistics and a description of the dataset, we perform a detailed analysis of 5 different models' predictions, and compare these with human performance. We investigate the relative importance of language, static (2D) visual features, and moving (3D) visual features; the effects of increasing dataset size, the number of frames sampled; and of vocabulary size. We illustrate that: this task is not solvable by a language model alone; our model combining 2D and 3D visual information indeed provides the best result; all models perform significantly worse than human-level. We provide human evaluations for responses given by different models and find that accuracy on the MovieFIB evaluation corresponds well with human judgement. We suggest avenues for improving video models, and hope that the proposed dataset can be useful for measuring and encouraging progress in this very interesting field.

2017-07-20

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (published)

GuessWhat?! Visual Object Discovery through Multi-modal Dialogue

Harm de Vries

Florian Strub

A. Chandar

Olivier Pietquin

Hugo Larochelle

We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The… (see more) goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task. Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. We explain our design decisions in collecting the dataset and introduce the oracle and questioner tasks that are associated with the two players of the game. We prototyped deep learning models to establish initial baselines of the introduced tasks.

2017-07-20

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (published)

Multi-Modal Variational Encoder-Decoders

Iulian V. Serban

Alexander G. Ororbia II

Joelle Pineau

2017-04-23

arXiv.org (preprint)

openreview.net

Char2Wav: End-to-End Speech Synthesis

Jose Sotelo

2017-02-16

International Conference on Learning Representations (unknown)

openreview.net

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)

openreview.net

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)