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

Stagiaire de recherche - McGill

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

Doctorat - 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

Co-superviseur⋅e :

Maîtrise recherche - Polytechnique

Doctorat - UdeM

Co-superviseur⋅e :

Yousef Kotp

Maîtrise recherche - Concordia

Co-superviseur⋅e :

Collaborateur·rice de recherche - UdeM

Maîtrise recherche - UdeM

Olga Luo

Doctorat - UdeM

Joel Moniz

Doctorat - Polytechnique

Jonathan Pilault

Doctorat - Polytechnique

Juan Rodriguez

Doctorat - École de technologie suprérieure

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

Spécification directe du comportement par apprentissage par renforcement sous contrainte

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

Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study

Chinnadhurai Sankar

Sandeep Subramanian

Sarath Chandar

Neural generative models have been become increasingly popular when building conversational agents. They offer flexibility, can be easily ad… (voir plus)apted to new domains, and require minimal domain engineering. A common criticism of these systems is that they seldom understand or use the available dialog history effectively. In this paper, we take an empirical approach to understanding how these models use the available dialog history by studying the sensitivity of the models to artificially introduced unnatural changes or perturbations to their context at test time. We experiment with 10 different types of perturbations on 4 multi-turn dialog datasets and find that commonly used neural dialog architectures like recurrent and transformer-based seq2seq models are rarely sensitive to most perturbations such as missing or reordering utterances, shuffling words, etc. Also, by open-sourcing our code, we believe that it will serve as a useful diagnostic tool for evaluating dialog systems in the future.

2019-07-01

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (publié)

doi.org

Towards Standardization of Data Licenses: The Montreal Data License

Misha Benjamin

P. Gagnon

Negar Rostamzadeh

Alex Shee

This paper provides a taxonomy for the licensing of data in the fields of artificial intelligence and machine learning. The paper's goal is … (voir plus)to build towards a common framework for data licensing akin to the licensing of open source software. Increased transparency and resolving conceptual ambiguities in existing licensing language are two noted benefits of the approach proposed in the paper. In parallel, such benefits may help foster fairer and more efficient markets for data through bringing about clearer tools and concepts that better define how data can be used in the fields of AI and ML. The paper's approach is summarized in a new family of data license language - \textit{the Montreal Data License (MDL)}. Alongside this new license, the authors and their collaborators have developed a web-based tool to generate license language espousing the taxonomies articulated in this paper.

2019-03-21

ArXiv (prépublication)

Deep Learning for Automated Segmentation of Liver Lesions at CT in Patients with Colorectal Cancer Liver Metastases.

Eugene Vorontsov

Milena Cerny

Philippe Régnier

Lisa Di Jorio

Réal Lapointe

Franck Vandenbroucke-Menu

Simon Turcotte

Samuel Kadoury

An Tang

Purpose To evaluate the performance, agreement, and efficiency of a fully convolutional network (FCN) for liver lesion detection and segment… (voir plus)ation at CT examinations in patients with colorectal liver metastases (CLMs). Materials and Methods This retrospective study evaluated an automated method using an FCN that was trained, validated, and tested with 115, 15, and 26 contrast material-enhanced CT examinations containing 261, 22, and 105 lesions, respectively. Manual detection and segmentation by a radiologist was the reference standard. Performance of fully automated and user-corrected segmentations was compared with that of manual segmentations. The interuser agreement and interaction time of manual and user-corrected segmentations were assessed. Analyses included sensitivity and positive predictive value of detection, segmentation accuracy, Cohen κ, Bland-Altman analyses, and analysis of variance. Results In the test cohort, for lesion size smaller than 10 mm (n = 30), 10-20 mm (n = 35), and larger than 20 mm (n = 40), the detection sensitivity of the automated method was 10%, 71%, and 85%; positive predictive value was 25%, 83%, and 94%; Dice similarity coefficient was 0.14, 0.53, and 0.68; maximum symmetric surface distance was 5.2, 6.0, and 10.4 mm; and average symmetric surface distance was 2.7, 1.7, and 2.8 mm, respectively. For manual and user-corrected segmentation, κ values were 0.42 (95% confidence interval: 0.24, 0.63) and 0.52 (95% confidence interval: 0.36, 0.72); normalized interreader agreement for lesion volume was -0.10 ± 0.07 (95% confidence interval) and -0.10 ± 0.08; and mean interaction time was 7.7 minutes ± 2.4 (standard deviation) and 4.8 minutes ± 2.1 (P .001), respectively. Conclusion Automated detection and segmentation of CLM by using deep learning with convolutional neural networks, when manually corrected, improved efficiency but did not substantially change agreement on volumetric measurements.© RSNA, 2019Supplemental material is available for this article.

2019-03-01

Radiology: Artificial Intelligence (publié)

doi.org

On Adversarial Mixup Resynthesis

Christopher Beckham

Sina Honari

Alex Lamb

Vikas Verma

Farnoosh Ghadiri

(Rex) Devon Hjelm

In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We explore mo… (voir plus)dels that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.

Adversarial Mixup Resynthesizers

Christopher Beckham

Sina Honari

Alex Lamb

Vikas Verma

Farnoosh Ghadiri

(Rex) Devon Hjelm

In this paper, we explore new approaches to combining information encoded within the learned representations of autoencoders. We explore mod… (voir plus)els that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.

2019-01-01

DGS@ICLR (publié)

openreview.net

Neural Multisensory Scene Inference

Jae Hyun Lim

Pedro O. Pinheiro

Negar Rostamzadeh

Sungjin Ahn

For embodied agents to infer representations of the underlying 3D physical world they inhabit, they should efficiently combine multisensory … (voir plus)cues from numerous trials, e.g., by looking at and touching objects. Despite its importance, multisensory 3D scene representation learning has received less attention compared to the unimodal setting. In this paper, we propose the Generative Multisensory Network (GMN) for learning latent representations of 3D scenes which are partially observable through multiple sensory modalities. We also introduce a novel method, called the Amortized Product-of-Experts, to improve the computational efficiency and the robustness to unseen combinations of modalities at test time. Experimental results demonstrate that the proposed model can efficiently infer robust modality-invariant 3D-scene representations from arbitrary combinations of modalities and perform accurate cross-modal generation. To perform this exploration we have also developed a novel multi-sensory simulation environment for embodied agents.

Real-Time Reinforcement Learning

Simon Ramstedt

Recurrent transition networks for character locomotion

Félix Harvey

We present a novel approach, based on deep recurrent neural networks, to automatically generate transition animations given a past context o… (voir plus)f a few frames, a target character state and optionally local terrain information. The proposed Recurrent Transition Network (RTN) is trained without any gait, phase, contact or action labels. Our system produces realistic and fluid transitions that rival the quality of Motion Capture-based animations, even without any inverse-kinematics post-process. Our system could accelerate the creation of transition variations for large coverage or even replace transition nodes in a game's animation graph. The RTN also shows impressive results on a temporal super-resolution task.

2018-12-04

SIGGRAPH Asia 2018 Technical Briefs (publié)

doi.org

Deep Learning recognizes weather and climate patterns

Karthik Kashinath

M. Prabhat

Mayur Mudigonda

Ankur Mahesh

Sookyung Kim

Yunjie Liu

Samira Ebrahimi Kahou

B. Toms

Evan Racah

Christopher Beckham

Tegan Maharaj

Jim Biard

K. Kunkel

Dean Nesbit Williams

Travis O'Brien

M. Wehner

W. Collins

A Survey of Mobile Computing for the Visually Impaired

Martin Weiss

Margaux Luck

Roger Girgis

Joseph Paul Cohen

The number of visually impaired or blind (VIB) people in the world is estimated at several hundred million. Based on a series of interviews … (voir plus)with the VIB and developers of assistive technology, this paper provides a survey of machine-learning based mobile applications and identifies the most relevant applications. We discuss the functionality of these apps, how they align with the needs and requirements of the VIB users, and how they can be improved with techniques such as federated learning and model compression. As a result of this study we identify promising future directions of research in mobile perception, micro-navigation, and content-summarization.

2018-11-25

ArXiv (prépublication)

Probabilistic Planning with Sequential Monte Carlo methods

Alexandre Piché

Valentin Thomas

Cyril Ibrahim

2018-09-27

International Conference on Learning Representations (publié)

openreview.net

Focused Hierarchical RNNs for Conditional Sequence Processing

Nan Rosemary Ke

Konrad Żołna

Alessandro Sordoni

Zhouhan Lin

Adam Trischler

Recurrent Neural Networks (RNNs) with attention mechanisms have obtained state-of-the-art results for many sequence processing tasks. Most o… (voir plus)f these models use a simple form of encoder with attention that looks over the entire sequence and assigns a weight to each token independently. We present a mechanism for focusing RNN encoders for sequence modelling tasks which allows them to attend to key parts of the input as needed. We formulate this using a multi-layer conditional sequence encoder that reads in one token at a time and makes a discrete decision on whether the token is relevant to the context or question being asked. The discrete gating mechanism takes in the context embedding and the current hidden state as inputs and controls information flow into the layer above. We train it using policy gradient methods. We evaluate this method on several types of tasks with different attributes. First, we evaluate the method on synthetic tasks which allow us to evaluate the model for its generalization ability and probe the behavior of the gates in more controlled settings. We then evaluate this approach on large scale Question Answering tasks including the challenging MS MARCO and SearchQA tasks. Our models shows consistent improvements for both tasks over prior work and our baselines. It has also shown to generalize significantly better on synthetic tasks as compared to the baselines.

2018-07-03

Proceedings of the 35th International Conference on Machine Learning (publié)

proceedings.mlr.press