Aaron Courville

sarvjeet-singh.ghotra@mila.quebec

Juan Duque

Doctorat - UdeM

juan.duque@mila.quebec

Doctorat - UdeM

Doctorat - UdeM

Uday Kapur

Doctorat - UdeM

udaykaran.kapur@mila.quebec

Amr Khalifa

Doctorat - UdeM

amr-khalifa.alshaykh@mila.quebec

samuel.lavoie@mila.quebec

Samuel Lavoie

Doctorat - UdeM

Zhixuan Lin

Doctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Simon Lacoste-Julien

alan.milligan@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Gauthier Gidel

andjela.mladenovic@mila.quebec

mohammed.muqeeth@mila.quebec

Google Scholar

Muqeeth Mohammed

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Rishabh Agarwal

morgane.moss@mila.quebec

Andrei Nicolicioiu

Doctorat - UdeM

andrei.nicolicioiu@gmail.com

Doctorat - UdeM

Johan Samir Obando Ceron

Doctorat - UdeM

Co-superviseur⋅e :

Pablo Samuel Castro

johan.ceron@mila.quebec

Collaborateur·rice de recherche - UdeM

michell.payanoperez@mila.quebec

Dereck Piché

Doctorat - UdeM

pichedereck@gmail.com

khaled.rouissi@mila.quebec

Khaled Rouissi

Maîtrise recherche - UdeM

Esra'a Saleh

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Glen Berseth

esraa.saleh@mila.quebec

Google Scholar

Vedant Shah

Doctorat - UdeM

vedant.shah@mila.quebec

Doctorat - UdeM

Yusong Wu

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Anna (Cheng-Zhi) Huang

wu.yusong@mila.quebec

Sujin yun

Doctorat - UdeM

sujin.yun@mila.quebec

xiaofeng.zhang@mila.quebec

Google Scholar

Xiaofeng Zhang

Doctorat - UdeM

Publications

Investigating Biases in Textual Entailment Datasets

The ability to understand logical relationships between sentences is an important task in language understanding. To aid in progress for thi… (voir plus)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.

2019-06-22

ArXiv (prépublication)

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… (voir plus) 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.

2019-06-08

ArXiv (prépublication)

Hierarchical Importance Weighted Autoencoders

Chin-wei Huang

Kris Sankaran

Eeshan Dhekane

Alexandre Lacoste

Importance weighted variational inference (Burda et al., 2015) uses multiple i.i.d. samples to have a tighter variational lower bound. We be… (voir plus)lieve a joint proposal has the potential of reducing the number of redundant samples, and introduce a hierarchical structure to induce correlation. The hope is that the proposals would coordinate to make up for the error made by one another to reduce the variance of the importance estimator. Theoretically, we analyze the condition under which convergence of the estimator variance can be connected to convergence of the lower bound. Empirically, we confirm that maximization of the lower bound does implicitly minimize variance. Further analysis shows that this is a result of negative correlation induced by the proposed hierarchical meta sampling scheme, and performance of inference also improves when the number of samples increases.

2019-05-23

International Conference on Machine Learning (publié)

proceedings.mlr.press

Representation Mixing for TTS Synthesis

Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. Ho… (voir plus)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.

2019-05-11

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

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

Yikeng Shen

Shawn Tan

Alessandro Sordoni

2019-05-05

(publié)

www.semanticscholar.org

Counterpoint by Convolution

Cheng-Zhi Anna Huang

Tim Cooijmans

Adam Roberts

Douglas Eck

Machine learning models of music typically break down the task of composition into a chronological process, composing a piece of music in a … (voir plus)single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. We explore the use of blocked Gibbs sampling as an analogue to the human approach, and introduce Coconet, a convolutional neural network in the NADE family of generative models. Despite ostensibly sampling from the same distribution as the NADE ancestral sampling procedure, we find that a blocked Gibbs approach significantly improves sample quality. We provide evidence that this is due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from Yao et al. (2014) yields better samples than ancestral sampling. We demonstrate the versatility of our method on unconditioned polyphonic music generation.

2019-03-17

ArXiv (prépublication)

openreview.net

Maximum Entropy Generators for Energy-Based Models

Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient. In t… (voir plus)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.

2019-01-23

ArXiv (prépublication)

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

Kundan Kumar

Rithesh Kumar

Thibault de Boissiere

Lucas Gestin

Wei Zhen Teoh

Jose Sotelo

Alexandre De Brébisson

Yoshua Bengio

2018-12-31

Neural Information Processing Systems (publié)

Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger c… (voir plus)onstituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such an inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.

2018-12-31

ICLR.cc/2019/Conference (présentation orale)

openreview.net

No Press Diplomacy: Modeling Multi-Agent Gameplay

Philip Paquette

Yuchen Lu

Steven Bocco

Max O. Smith

Satya Ortiz-Gagné

Jonathan K. Kummerfeld

Satinder Singh

Joelle Pineau

Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal. Rel… (voir plus)iance on trust and coordination makes Diplomacy the first non-cooperative multi-agent benchmark for complex sequential social dilemmas in a rich environment. In this work, we focus on training an agent that learns to play the No Press version of Diplomacy where there is no dedicated communication channel between players. We present DipNet, a neural-network-based policy model for No Press Diplomacy. The model was trained on a new dataset of more than 150,000 human games. Our model is trained by supervised learning (SL) from expert trajectories, which is then used to initialize a reinforcement learning (RL) agent trained through self-play. Both the SL and RL agents demonstrate state-of-the-art No Press performance by beating popular rule-based bots.

2018-12-31

Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (publié)