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 :

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 :

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

StarVector: Generating Scalable Vector Graphics Code from Images and Text

Juan A. Rodriguez

Abhay Puri

Shubham Agarwal

Issam Hadj Laradji

Pau Rodriguez

Sai Rajeswar

David Vazquez

Marco Pedersoli

2025-04-11

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery

Amirhossein Abaskohi

Amrutha Varshini Ramesh

Shailesh Nanisetty

Chirag Goel

David Vazquez

Spandana Gella

Giuseppe Carenini

Issam Hadj Laradji

2025-04-10

ArXiv (prépublication)

AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery

Amirhossein Abaskohi

Amrutha Varshini Ramesh

Shailesh Nanisetty

Chirag Goel

David Vazquez

Spandana Gella

Giuseppe Carenini

Issam Hadj Laradji

2025-04-10

ArXiv (prépublication)

LitLLMs, LLMs for Literature Review: Are we there yet?

Shubham Agarwal

Gaurav Sahu

Abhay Puri

Issam Hadj Laradji

Krishnamurthy Dj Dvijotham

Jason Stanley

Laurent Charlin

2025-04-01

TMLR (accepté)

Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments

Luke Rowe

Roger Girgis

Anthony Gosselin

Liam Paull

Felix Heide

2025-03-28

ArXiv (prépublication)

StarFlow: Generating Structured Workflow Outputs From Sketch Images

Patrice Bechard

Chao Wang

Amirhossein Abaskohi

Juan A. Rodriguez

David Vazquez

Spandana Gella

Sai Rajeswar

Perouz Taslakian

2025-03-27

ArXiv (prépublication)

StarFlow: Generating Structured Workflow Outputs From Sketch Images

Patrice Bechard

Chao Wang

Amirhossein Abaskohi

Juan A. Rodriguez

David Vazquez

Spandana Gella

Sai Rajeswar

Perouz Taslakian

2025-03-27

ArXiv (prépublication)

UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Juan A. Rodriguez

Montek Kalsi

Rabiul Awal

Nicolas Chapados

M. T. ¨Ozsu

Aishwarya Agrawal

David Vazquez

Perouz Taslakian

Spandana Gella

Sai Rajeswar

Human Annotator

2025-03-19

ArXiv (prépublication)

AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding

Ahmed Masry

Juan A. Rodriguez

Tianyu Zhang

Suyuchen Wang

Chao Wang

Aarash Feizi

Akshay Kalkunte Suresh

Abhay Puri

Xiangru Jian

Pierre-Andre Noel

Sathwik Tejaswi Madhusudhan

Enamul Hoque

Issam Hadj Laradji

David Vazquez

Perouz Taslakian … (voir 2 de plus)

Spandana Gella

Sai Rajeswar

Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges… (voir plus) on having a good connector that maps visual features generated by a vision encoder to a shared embedding space with the LLM while preserving semantic similarity. Existing connectors, such as multilayer perceptrons (MLPs), often produce out-of-distribution or noisy inputs, leading to misalignment between the modalities. In this work, we propose a novel vision-text alignment method, AlignVLM, that maps visual features to a weighted average of LLM text embeddings. Our approach leverages the linguistic priors encoded by the LLM to ensure that visual features are mapped to regions of the space that the LLM can effectively interpret. AlignVLM is particularly effective for document understanding tasks, where scanned document images must be accurately mapped to their textual content. Our extensive experiments show that AlignVLM achieves state-of-the-art performance compared to prior alignment methods. We provide further analysis demonstrating improved vision-text feature alignment and robustness to noise.

2025-03-05

ICLR.cc/2025/Workshop/Re-Align (poster)

doi.org

WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation

Rabiul Awal

Mahsa Massoud

Zichao Li

Aarash Feizi

Suyuchen Wang

Aishwarya Agrawal

David Vazquez

Siva Reddy

Juan A. Rodriguez

Perouz Taslakian

Spandana Gella

Sai Rajeswar

Understanding diverse web data and automating web development presents an exciting challenge for agentic AI. While existing benchmarks addre… (voir plus)ss isolated web-based tasks—such as website-based Visual Question Answering (VQA) and UI-to-code generation—they lack a unified evaluation suite for assessing web agents that interact with and reason about web environments. We introduce WebMMU, a large-scale benchmark for evaluating AI-driven web agents across multilingual website VQA, HTML/CSS/JavaScript code editing, and sketch-to-code generation. WebMMU provides a comprehensive evaluation suite with real-world website data, multi-step reasoning tasks, and functional UI understanding. Benchmarking state-of-the-art multimodal models on WebMMU reveals significant limitations in web-based reasoning, layout understanding, and structured code generation, particularly in preserving UI hierarchy, handling multilingual content, and producing robust, functional code. While most existing models are optimized for English-only settings, WebMMU highlights the challenges of cross-lingual adaptation in real-world web development. These findings expose critical gaps in current models’ ability to understand website structures, execute user instructions, and generate high-quality web code, underscoring the need for more advanced multimodal reasoning in AI-driven web understanding and development.

2025-03-05

ICLR.cc/2025/Workshop/DL4C (publié)

UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Juan A. Rodriguez

Montek Kalsi

Rabiul Awal

Nicolas Chapados

M. T. ¨Ozsu

Aishwarya Agrawal

David Vazquez

Perouz Taslakian

Spandana Gella

Sai Rajeswar

Human Annotator

2025-03-01

arXiv (publié)

doi.org

Beyond FVD: An Enhanced Evaluation Metrics for Video Generation Distribution Quality

Ge Ya Luo

Gian Mario Favero

Zhi Hao Luo

Alexia Jolicoeur-Martineau

The Fréchet Video Distance (FVD) is a widely adopted metric for evaluating video generation distribution quality. However, its effectivenes… (voir plus)s relies on critical assumptions. Our analysis reveals three significant limitations: (1) the non-Gaussianity of the Inflated 3D Convnet (I3D) feature space; (2) the insensitivity of I3D features to temporal distortions; (3) the impractical sample sizes required for reliable estimation. These findings undermine FVD's reliability and show that FVD falls short as a standalone metric for video generation evaluation. After extensive analysis of a wide range of metrics and backbone architectures, we propose JEDi, the JEPA Embedding Distance, based on features derived from a Joint Embedding Predictive Architecture, measured using Maximum Mean Discrepancy with polynomial kernel. Our experiments on multiple open-source datasets show clear evidence that it is a superior alternative to the widely used FVD metric, requiring only 16% of the samples to reach its steady value, while increasing alignment with human evaluation by 34%, on average. Project page: https://oooolga.github.io/JEDi.github.io/.

2025-01-22

ICLR.cc/2025/Conference (poster)