Doina Precup

guangyuan.wang@mila.quebec

Guangyuan Wang

Research Intern - McGill University

Haque Ishfaq

PhD - McGill University

PhD - McGill University

huanghow@mila.quebec

Janarthanan Rajendran

Postdoctorate - Université de Montréal

Principal supervisor :

Sarath Chandar Anbil Parthipan

janarthanan.rajendran@mila.quebec

jonathan.colaco-carr@mila.quebec

Jaume Minano Masip

PhD - McGill University

masipmij@mila.quebec

Jesse Farebrother

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

Master's Research - McGill University

Principal supervisor :

Prakash Panangaden

Jonathan Lebensold

PhD - McGill University

Research Intern - McGill University

keyu.wang@mila.quebec

Kushal Arora

PhD - McGill University

Principal supervisor :

Lynn Cherif

Master's Research - McGill University

Co-supervisor :

Khimya Khetarpal

lynn.cherif@mila.quebec

Mohammad Sami Nur Islam Islam

Mandana Samiei

PhD - McGill University

Co-supervisor :

PhD - McGill University

delvermm@mila.quebec

Martin Klissarov

PhD - McGill University

Harry Zhao

PhD - McGill University

Co-supervisor :

Research Intern - McGill University

mohammad-sami-nur.islam@mila.quebec

nathan.de-lara@mila.quebec

Nathan de Lara

Research Intern - McGill University

Nate Rahn

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

nathan.rahn@mila.quebec

Girdhar Neil Girdhar

Collaborating researcher - McGill University

neil.girdhar@mila.quebec

Nikhil Vemgal

Master's Research - McGill University

nikhil-murali.vemgal@mila.quebec

padideh.nouri@mila.quebec

Nishanth Anand Vemgal

PhD - McGill University

Master's Research - Université de Montréal

PhD - McGill University

Ray Chua

PhD - McGill University

Co-supervisor :

Blake Richards

chuaraym@mila.quebec

Riashat Islam

PhD - McGill University

Safa Alver

PhD - McGill University

alversaf@mila.quebec

Sahand Rezaei-Shoshtari

PhD - McGill University

Co-supervisor :

David Meger

sahand.rezaei-shoshtari@mila.quebec

PhD - McGill University

PhD - McGill University

Co-supervisor :

David Meger

fujimots@mila.quebec

Shahrad Mohammadzadeh

Collaborating researcher - McGill University

Principal supervisor :

Reihaneh Rabbany

shahrad.mohammadzadeh@mila.quebec

PhD - McGill University

PhD - McGill University

shuyuan.zhang@mila.quebec

Sitao Luan

PhD - McGill University

Steve Wen

Undergraduate - McGill University

steve.wen@mila.quebec

Sumana Basu

PhD - McGill University

Co-supervisor :

Adriana Romero Soriano

Master's Research - Université de Montréal

Principal supervisor :

Yoshua Bengio

thomas.jiralerspong@mila.quebec

PhD - McGill University

cheluver@mila.quebec

Wesley Chung

PhD - McGill University

Principal supervisor :

David Meger

chungwes@mila.quebec

Ray Luo

PhD - McGill University

Principal supervisor :

Xujie Si

luo.ziyan@mila.quebec

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

Read the article

Publications

Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning

Tianyu Li

In this paper, we unravel a fundamental connection between weighted finite automata~(WFAs) and second-order recurrent neural networks~(2-RNN… (see more)s): in the case of sequences of discrete symbols, WFAs and 2-RNNs with linear activation functions are expressively equivalent. Motivated by this result, we build upon a recent extension of the spectral learning algorithm to vector-valued WFAs and propose the first provable learning algorithm for linear 2-RNNs defined over sequences of continuous input vectors. This algorithm relies on estimating low rank sub-blocks of the so-called Hankel tensor, from which the parameters of a linear 2-RNN can be provably recovered. The performances of the proposed method are assessed in a simulation study.

2018-07-04

ArXiv (preprint)

arxiv.org

Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization

Kian Kenyon-Dean

Jackie Cheung

We present an approach to event coreference resolution by developing a general framework for clustering that uses supervised representation … (see more)learning. We propose a neural network architecture with novel Clustering-Oriented Regularization (CORE) terms in the objective function. These terms encourage the model to create embeddings of event mentions that are amenable to clustering. We then use agglomerative clustering on these embeddings to build event coreference chains. For both within- and cross-document coreference on the ECB+ corpus, our model obtains better results than models that require significantly more pre-annotated information. This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning.

2018-06-01

Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics (published)

arxiv.org

Nonlinear Weighted Finite Automata

Tianyu Li

Weighted finite automata (WFA) can expressively model functions defined over strings but are inherently linear models. Given the recent succ… (see more)esses of nonlinear models in machine learning, it is natural to wonder whether extending WFA to the nonlinear setting would be beneficial. In this paper, we propose a novel model of neural network based nonlinear WFA model (NL-WFA) along with a learning algorithm. Our learning algorithm is inspired by the spectral learning algorithm for WFA and relies on a nonlinear decomposition of the so-called Hankel matrix, by means of an auto-encoder network. The expressive power of NL-WFA and the proposed learning algorithm are assessed on both synthetic and real world data, showing that NL-WFA can lead to smaller model sizes and infer complex grammatical structures from data.

2018-03-31

International Conference on Artificial Intelligence and Statistics (published)

dblp.uni-trier.de

Nonlinear Weighted Finite Automata

Tianyu Li

2018-01-01

AISTATS (published)

proceedings.mlr.press

Optimizing Home Energy Management and Electric Vehicle Charging with Reinforcement Learning

Di Wu

Vincent Francois-Lavet

Benoit Boulet

Smart grids are advancing the management efficiency and security of power grids with the integration of energy storage, distributed controll… (see more)ers, and advanced meters. In particular, with the increasing prevalence of residential automation devices and distributed renewable energy generation, residential energy management is now drawing more attention. Meanwhile, the increasing adoption of electric vehicle (EV) brings more challenges and opportunities for smart residential energy management. This paper formalizes energy management for the residential home with EV charging as a Markov Decision Process and proposes reinforcement learning (RL) based control algorithms to address it. The objective of the proposed algorithms is to minimize the long-term operating cost. We further use a recurrent neural network (RNN) to model the electricity demand as a preprocessing step. Both the RNN prediction and latent representations are used as additional state features for the RL based control algorithms. Experiments on real-world data show that the proposed algorithms can significantly reduce the operating cost and peak power consumption compared to baseline control algorithms.

2018-01-01

(published)

www.semanticscholar.org

Temporal Regularization for Markov Decision Process

Pierre Thodoroff

Audrey Durand

Joelle Pineau

Several applications of Reinforcement Learning suffer from instability due to high variance. This is especially prevalent in high dimensiona… (see more)l domains. Regularization is a commonly used technique in machine learning to reduce variance, at the cost of introducing some bias. Most existing regularization techniques focus on spatial (perceptual) regularization. Yet in reinforcement learning, due to the nature of the Bellman equation, there is an opportunity to also exploit temporal regularization based on smoothness in value estimates over trajectories. This paper explores a class of methods for temporal regularization. We formally characterize the bias induced by this technique using Markov chain concepts. We illustrate the various characteristics of temporal regularization via a sequence of simple discrete and continuous MDPs, and show that the technique provides improvement even in high-dimensional Atari games.

Neural Network Based Nonlinear Weighted Finite Automata

Tianyu Li

Weighted finite automata (WFA) can expressively model functions defined over strings but are inherently linear models. Given the recent succ… (see more)esses of nonlinear models in machine learning, it is natural to wonder whether ex-tending WFA to the nonlinear setting would be beneficial. In this paper, we propose a novel model of neural network based nonlinearWFA model (NL-WFA) along with a learning algorithm. Our learning algorithm is inspired by the spectral learning algorithm for WFAand relies on a nonlinear decomposition of the so-called Hankel matrix, by means of an auto-encoder network. The expressive power of NL-WFA and the proposed learning algorithm are assessed on both synthetic and real-world data, showing that NL-WFA can lead to smaller model sizes and infer complex grammatical structures from data.

2017-09-13

ArXiv (preprint)

arxiv.org

Predicting Future Disease Activity and Treatment Responders for Multiple Sclerosis Patients Using a Bag-of-Lesions Brain Representation

Andrew Doyle

Douglas Arnold

Tal Arbel

2017-09-10

International Conference on Medical Image Computing and Computer-Assisted Intervention (published)

World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions

Teng Long

Emmanuel Bengio

Ryan Lowe

Jackie Cheung

Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading compr… (see more)ehension systems that can do the same. In this paper, we introduce a task and several models to drive progress towards this goal. In particular, we propose the task of rare entity prediction: given a web document with several entities removed, models are tasked with predicting the correct missing entities conditioned on the document context and the lexical resources. This task is challenging due to the diversity of language styles and the extremely large number of rare entities. We propose two recurrent neural network architectures which make use of external knowledge in the form of entity descriptions. Our experiments show that our hierarchical LSTM model performs significantly better at the rare entity prediction task than those that do not make use of external resources.

2017-09-01

Conference on Empirical Methods in Natural Language Processing (published)

Bayesian and grAphical Models for Biomedical Imaging

M. Cardoso

Ivor J. A. Simpson

Tal Arbel

Annemie Ribbens

2014-01-01

Lecture Notes in Computer Science (published)