Chris Pal

Membre académique principal

christopher.pal@mila.quebec

Chaire en IA Canada-CIFAR

Professeur titulaire, Polytechnique Montréal, Département de génie informatique et de génie logiciel

Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle

Sujets de recherche

Apprentissage profond

Site web

Google Scholar

Biographie

Christopher Pal est titulaire d'une chaire en IA Canada-CIFAR, professeur titulaire à Polytechnique Montréal et professeur adjoint au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal. Il est également chercheur émérite à ServiceNow Research. Il est engagé dans la recherche sur l'intelligence artificielle et l'apprentissage automatique depuis plus de 25 ans, publiant souvent des travaux sur les méthodes de modélisation du langage à grande échelle et les techniques de modélisation générative. Il a obtenu un doctorat en informatique à l'Université de Waterloo.

Étudiants actuels

Mai Ababneh

Collaborateur·rice de recherche - Formerly McGill (but ending)

Paul Barde

Collaborateur·rice de recherche - McGill

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Github

Can (Sam) Chen

Collaborateur·rice alumni - McGill

Superviseur⋅e principal⋅e :

Xue (Steve) Liu

Site web

Léa Demeule

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Chris Emezue

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Doctorat - Polytechnique

Simon Guiroy

Doctorat - UdeM

Co-superviseur⋅e :

Yousef Kotp

Maîtrise recherche - Concordia

Co-superviseur⋅e :

Doctorat - Polytechnique

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Site web

Github

Google Scholar

Olga Luo

Doctorat - UdeM

Doctorat - UdeM

Joel Moniz

Doctorat - Polytechnique

Juan Rodriguez

Doctorat - École de technologie suprérieure

Site web

Github

Google Scholar

Luke Rowe

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Gaurav Sahu

Postdoctorat - HEC

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - McGill

Superviseur⋅e principal⋅e :

Postdoctorat - Polytechnique

Co-superviseur⋅e :

Doctorat - UdeM

Collaborateur·rice de recherche

Github

Billets de blogue

Direct Behavior Specification via Constrained Reinforcement Learning

31 août 2022

Spécification directe du comportement par apprentissage par renforcement sous contrainte

par

Julien Roy

Roger Girgis

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Lire l'article

Publications

Towards Deep Conversational Recommendations

Raymond Li

Samira Ebrahimi Kahou

Hannes Schulz

Vincent Michalski

Laurent Charlin

Chris Pal

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendat… (voir plus)ion is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale dataset consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a dataset consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms, and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.

2017-12-31

Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (publié)

doi.org

arxiv.org

Towards Text Generation with Adversarially Learned Neural Outlines.

Sandeep Subramanian

Sai Rajeswar

Alessandro Sordoni

Adam Trischler

Aaron C. Courville

Chris Pal

2017-12-31

Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (publié)

dblp.uni-trier.de

Actual: Actor-Critic Under Adversarial Learning

Nan Rosemary Ke

R Devon Hjelm

Generative Adversarial Networks (GANs) are a powerful framework for deep generative modeling. Posed as a two-player minimax problem, GANs ar… (voir plus)e typically trained end-to-end on real-valued data and can be used to train a generator of high-dimensional and realistic images. However, a major limitation of GANs is that training relies on passing gradients from the discriminator through the generator via back-propagation. This makes it fundamentally difficult to train GANs with discrete data, as generation in this case typically involves a non-differentiable function. These difficulties extend to the reinforcement learning setting when the action space is composed of discrete decisions. We address these issues by reframing the GAN framework so that the generator is no longer trained using gradients through the discriminator, but is instead trained using a learned critic in the actor-critic framework with a Temporal Difference (TD) objective. This is a natural fit for sequence modeling and we use it to achieve improvements on language modeling tasks over the standard Teacher-Forcing methods.

2017-11-12

ArXiv (prépublication)

doi.org

arxiv.org

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks

Nan Rosemary Ke

A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagat… (voir plus)e credit information backwards through every single step of the forward computation. This makes BPTT both computationally impractical and biologically implausible. For this reason, full backpropagation through time is rarely used on long sequences, and truncated backpropagation through time is used as a heuristic. However, this usually leads to biased estimates of the gradient in which longer term dependencies are ignored. Addressing this issue, we propose an alternative algorithm, Sparse Attentive Backtracking, which might also be related to principles used by brains to learn long-term dependencies. Sparse Attentive Backtracking learns an attention mechanism over the hidden states of the past and selectively backpropagates through paths with high attention weights. This allows the model to learn long term dependencies while only backtracking for a small number of time steps, not just from the recent past but also from attended relevant past states.

2017-11-06

ArXiv (prépublication)

doi.org

openreview.net

Brain Tumor Segmentation with Deep Neural Networks