Chris Pal

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)

ababneh.mai@gmail.com

Shubham Agarwal

Postdoctorat - HEC

Superviseur⋅e principal⋅e :

Paul Barde

Collaborateur·rice de recherche - McGill

Superviseur⋅e principal⋅e :

Derek Nowrouzezahrai

paul.b.barde@gmail.com

Maîtrise recherche - UdeM

Chris Beckham

Doctorat - Polytechnique

Can (Sam) Chen

Collaborateur·rice alumni - McGill

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Chris Emezue

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Collaborateur·rice alumni - Polytechnique

Roger Girgis

Doctorat - Polytechnique

Florian Golemo

Postdoctorat - McGill

Maîtrise recherche - Polytechnique

Doctorat - UdeM

Co-superviseur⋅e :

Yousef Kotp

Maîtrise recherche - Concordia

Co-superviseur⋅e :

Collaborateur·rice de recherche - UdeM

Maîtrise recherche - UdeM

Site web

Olga Luo

Doctorat - UdeM

Doctorat - UdeM

Joel Moniz

Doctorat - Polytechnique

Jonathan Pilault

Doctorat - Polytechnique

Juan Rodriguez

Doctorat - École de technologie suprérieure

Site web

Spécification directe du comportement par apprentissage par renforcement sous contrainte

Luke Rowe

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Gaurav Sahu

Postdoctorat - HEC

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Doctorat - UdeM

Billets de blogue

Direct Behavior Specification via Constrained Reinforcement Learning

31 août 2022

par

Julien Roy

Roger Girgis

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Lire l'article

Publications

Self-organized Hierarchical Softmax

Yikang Shen

Shawn Tan

Aaron Courville

We propose a new self-organizing hierarchical softmax formulation for neural-network-based language models over large vocabularies. Instead … (voir plus)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.

2017-07-26

ArXiv (prépublication)

A Dataset and Exploration of Models for Understanding Video Data through Fill-in-the-Blank Question-Answering

Tegan Maharaj

Nicolas Ballas

Anna Rohrbach

Aaron Courville

While deep convolutional neural networks frequently approach or exceed human-level performance in benchmark tasks involving static images, e… (voir plus)xtending this success to moving images is not straightforward. Video understanding is of interest for many applications, including content recommendation, prediction, summarization, event/object detection, and understanding human visual perception. However, 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 evaluation for responses given by different models and find that accuracy on the MovieFIB evaluation corresponds well with human judgment. We suggest avenues for improving video models, and hope that the MovieFIB challenge can be useful for measuring and encouraging progress in this very interesting field.

2017-07-21

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

doi.org

Unimodal Probability Distributions for Deep Ordinal Classification

Christopher Beckham

Probability distributions produced by the cross-entropy loss for ordinal classification problems can possess undesired properties. We propos… (voir plus)e a straightforward technique to constrain discrete ordinal probability distributions to be unimodal via a combination of the Poisson probability mass function and the softmax nonlinearity. We evaluate this approach on two large ordinal image datasets and obtain promising results.

2017-07-17

Proceedings of the 34th International Conference on Machine Learning (publié)

proceedings.mlr.press

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (prépublication)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (prépublication)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (prépublication)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (prépublication)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (prépublication)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

2017-05-27

ArXiv (prépublication)

Deep Complex Networks

Chiheb Trabelsi

Dmitriy Serdyuk

Sandeep Subramanian

Joao Felipe Santos

Soroush Mehri

At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and re… (voir plus)presentations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capacity and could also facilitate noise-robust memory retrieval mechanisms. Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models. In this work, we provide the key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks and convolutional LSTMs. More precisely, we rely on complex convolutions and present algorithms for complex batch-normalization, complex weight initialization strategies for complex-valued neural nets and we use them in experiments with end-to-end training schemes. We demonstrate that such complex-valued models are competitive with their real-valued counterparts. We test deep complex models on several computer vision tasks, on music transcription using the MusicNet dataset and on Speech Spectrum Prediction using the TIMIT dataset. We achieve state-of-the-art performance on these audio-related tasks.

2017-05-27

ArXiv (prépublication)