Portrait de Chris Pal

Chris Pal

Membre académique principal
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

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

Stagiaire de recherche - McGill
Postdoctorat - HEC
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - McGill
Superviseur⋅e principal⋅e :
Maîtrise recherche - UdeM
Doctorat - Polytechnique
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - Polytechnique
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Collaborateur·rice alumni - Polytechnique
Doctorat - Polytechnique
Postdoctorat - McGill
Co-superviseur⋅e :
Maîtrise recherche - Polytechnique
Doctorat - UdeM
Co-superviseur⋅e :
Collaborateur·rice de recherche - UdeM
Maîtrise recherche - UdeM
Doctorat - UdeM
Doctorat - Polytechnique
Doctorat - Polytechnique
Doctorat - École de technologie suprérieure
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - Polytechnique
Superviseur⋅e principal⋅e :
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - Polytechnique

Publications

Distilling semantically aware orders for autoregressive image generation
Rishav Pramanik
Antoine Poupon
Juan A. Rodriguez
Masih Aminbeidokhti
David Vazquez
Zhaozheng Yin
Distilling semantically aware orders for autoregressive image generation
Rishav Pramanik
Antoine Poupon
Juan A. Rodriguez
Masih Aminbeidokhti
David Vazquez
Zhaozheng Yin
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
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
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
LitLLMs, LLMs for Literature Review: Are we there yet?
Shubham Agarwal
Gaurav Sahu
Abhay Puri
Issam Hadj Laradji
Krishnamurthy Dj Dvijotham
Jason Stanley
Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
Luke Rowe
Roger Girgis
Anthony Gosselin
Felix Heide
Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
Luke Rowe
Roger Girgis
Anthony Gosselin
Felix Heide
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
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
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
M. T. ¨Ozsu
David Vazquez
Perouz Taslakian
Spandana Gella
Sai Rajeswar
Human Annotator
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.