Doina Precup

Sumana Basu

PhD - McGill University

Co-supervisor :

Adriana Romero Soriano

PhD - McGill University

Derek Kweku DEGBEDZUI Degbedzui

Lynn Cherif

Master's Research - McGill University

Co-supervisor :

PhD - McGill University

Co-supervisor :

PhD - McGill University

Principal supervisor :

David Meger

Jonathan Colaço Carr

Master's Research - McGill University

Principal supervisor :

Prakash Panangaden

Élodie Coté-Gauthier

Research Intern - McGill University

Master's Research - McGill University

Manuel Del Verme

PhD - McGill University

Jesse Farebrother

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

PhD - McGill University

Principal supervisor :

Eilif Benjamin Muller

Will Hua

Master's Research - McGill University

Co-supervisor :

Howard Huang

PhD - McGill University

Haque Ishfaq

PhD - McGill University

Mohammad Sami Nur Islam Islam

Master's Research - McGill University

Riashat Islam

PhD - McGill University

Arushi Jain

PhD - McGill University

Master's Research - Université de Montréal

Principal supervisor :

PhD - McGill University

Postdoctorate - McGill University

Elaine Lau

Master's Research - McGill University

Jonathan Lebensold

Collaborating Alumni - McGill University

Sitao Luan

PhD - McGill University

Ray Luo

PhD - McGill University

Principal supervisor :

G McCracken

PhD - McGill University

Shahrad Mohammadzadeh

Master's Research - McGill University

Principal supervisor :

Gabriela Moisescu-Pareja

Master's Research - McGill University

Girdhar Neil Girdhar

Collaborating researcher - McGill University

Padideh Nouri

PhD - Université de Montréal

Co-supervisor :

Charles Onu

PhD - McGill University

PhD - McGill University

Co-supervisor :

Nate Rahn

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

Janarthanan Rajendran

Collaborating Alumni - Université de Montréal

Principal supervisor :

Sarath Chandar

Sahand Rezaei-Shoshtari

PhD - McGill University

Co-supervisor :

PhD - McGill University

Co-supervisor :

Blake Richards

samiemandana@gmail.com

PhD - McGill University

Co-supervisor :

PhD - McGill University

Nishanth Anand Vemgal

PhD - McGill University

PhD - McGill University

Priyesh Vijayan

PhD - McGill University

Co-supervisor :

Samira Ebrahimi Kahou

Research Intern - McGill University

Keyu Wang

Research Intern - McGill University

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Steve Wen

Undergraduate - McGill University

Co-supervisor :

Gregory Dudek

Shuyuan Zhang

PhD - McGill University

Harry Zhao

PhD - McGill University

Co-supervisor :

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

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Publications

CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds

David Budaghyan

Arsenii Gorin

Cem Subakan

Charles Onu

This paper describes the Ubenwa CryCeleb dataset - a labeled collection of infant cries - and the accompanying CryCeleb 2023 task, which is … (see more)a public speaker verification challenge based on cry sounds. We released more than 6 hours of manually segmented cry sounds from 786 newborns for academic use, aiming to encourage research in infant cry analysis. The inaugural public competition attracted 59 participants, 11 of whom improved the baseline performance. The top-performing system achieved a significant improvement scoring 25.8% equal error rate, which is still far from the performance of state-of-the-art adult speaker verification systems. Therefore, we believe there is room for further research on this dataset, potentially extending beyond the verification task.

2024-04-14

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)

Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks

Sitao Luan

Mingde Zhao

Xiao-Wen Chang

2024-02-20

Complex Networks & Their Applications XII (published)

When Do We Need Graph Neural Networks for Node Classification?

Sitao Luan

Chenqing Hua

Qincheng Lu

Jiaqi Zhu

Xiao-Wen Chang

2024-02-20

Complex Networks & Their Applications XII (published)

Gintare Karolina Dziugaite

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Johan Samir Obando Ceron

Ghada Sokar

Timon Willi

Clare Lyle

Jesse Farebrother

Jakob Nicolaus Foerster

Pablo Samuel Castro

The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance s… (see more)cales proportionally to its size. Analogous scaling laws remain elusive for reinforcement learning domains, however, where increasing the parameter count of a model often hurts its final performance. In this paper, we demonstrate that incorporating Mixture-of-Expert (MoE) modules, and in particular Soft MoEs (Puigcerver et al., 2023), into value-based networks results in more parameter-scalable models, evidenced by substantial performance increases across a variety of training regimes and model sizes. This work thus provides strong empirical evidence towards developing scaling laws for reinforcement learning.

2024-02-13

ArXiv (preprint)

Gintare Karolina Dziugaite

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Johan Samir Obando Ceron

Ghada Sokar

Timon Willi

Clare Lyle

Jesse Farebrother

Jakob Nicolaus Foerster

Pablo Samuel Castro

2024-02-13

ArXiv (preprint)

Gintare Karolina Dziugaite

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Johan Samir Obando Ceron

Ghada Sokar

Timon Willi

Clare Lyle

Jesse Farebrother

Jakob Nicolaus Foerster

Pablo Samuel Castro

2024-02-13

ArXiv (preprint)

On the Privacy of Selection Mechanisms with Gaussian Noise

Jonathan Lebensold

Borja Balle

2024-02-09

ArXiv (preprint)

Code as Reward: Empowering Reinforcement Learning with VLMs

David Venuto

Mohammad Sami Nur Islam

Martin Klissarov

Sherry Yang

Ankit Anand

Pre-trained Vision-Language Models (VLMs) are able to understand visual concepts, describe and decompose complex tasks into sub-tasks, and p… (see more)rovide feedback on task completion. In this paper, we aim to leverage these capabilities to support the training of reinforcement learning (RL) agents. In principle, VLMs are well suited for this purpose, as they can naturally analyze image-based observations and provide feedback (reward) on learning progress. However, inference in VLMs is computationally expensive, so querying them frequently to compute rewards would significantly slowdown the training of an RL agent. To address this challenge, we propose a framework named Code as Reward (VLM-CaR). VLM-CaR produces dense reward functions from VLMs through code generation, thereby significantly reducing the computational burden of querying the VLM directly. We show that the dense rewards generated through our approach are very accurate across a diverse set of discrete and continuous environments, and can be more effective in training RL policies than the original sparse environment rewards.

2024-02-07

ArXiv (preprint)

Effective Protein-Protein Interaction Exploration with PPIretrieval

Chenqing Hua

Connor Coley

Shuangjia Zheng

2024-02-06

ArXiv (preprint)

Effective Protein-Protein Interaction Exploration with PPIretrieval

Chenqing Hua

Connor W. Coley

Shuangjia Zheng

Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, an… (see more)d immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the challenge has shifted towards effectively navigating the vast complex universe to identify potential PPIs. Herein, we propose PPIretrieval, the first deep learning-based model for protein-protein interaction exploration, which leverages existing PPI data to effectively search for potential PPIs in an embedding space, capturing rich geometric and chemical information of protein surfaces. When provided with an unseen query protein with its associated binding site, PPIretrieval effectively identifies a potential binding partner along with its corresponding binding site in an embedding space, facilitating the formation of protein-protein complexes.

2024-02-06

ArXiv (preprint)

Effective Protein-Protein Interaction Exploration with PPIretrieval

Chenqing Hua

Connor W. Coley

Shuangjia Zheng

2024-02-06

ArXiv (preprint)

Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning

Harry Zhao 0001

Harry Zhao

Mingde Zhao

Safa Alver

Harm van Seijen

Romain Laroche