Portrait de Irina Rish

Irina Rish

Membre académique principal
Chaire en IA Canada-CIFAR
Professeure titulaire, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Apprentissage en ligne
Apprentissage multimodal
Apprentissage par renforcement
Apprentissage profond
Modèles génératifs
Neurosciences computationnelles
Traitement du langage naturel

Biographie

Irina Rish est professeure titulaire à l'Université de Montréal (UdeM), où elle dirige le Laboratoire d'IA autonome. Membre du corps professoral de Mila – Institut québécois d’intelligence artificielle, elle est titulaire d'une chaire d'excellence en recherche du Canada (CERC) et d'une chaire en IA Canada-CIFAR. Irina dirige le projet INCITE du ministère américain de l'Environnement au sujet des modèles de fondation évolutifs sur les superordinateurs Summit et Frontier à l'Oak Ridge Leadership Computing Facility (OLCF). Elle est cofondatrice et directrice scientifique de Nolano.ai.

Ses recherches actuelles portent sur les lois de mise à l'échelle neuronale et les comportements émergents (capacités et alignement) dans les modèles de fondation, ainsi que sur l'apprentissage continu, la généralisation hors distribution et la robustesse. Avant de se joindre à l'UdeM en 2019, Irina était chercheuse au Centre de recherche IBM Thomas J. Watson, où elle a travaillé sur divers projets à l'intersection des neurosciences et de l'IA, et dirigé le défi NeuroAI. Elle a reçu plusieurs prix IBM : ceux de l’excellence et de l’innovation exceptionnelle (2018), celui de la réalisation technique exceptionnelle (2017), et celui de l’accomplissement en recherche (2009). Elle détient 64 brevets et a écrit plus de 120 articles de recherche, plusieurs chapitres de livres, trois livres publiés et une monographie sur la modélisation éparse.

Étudiants actuels

Visiteur de recherche indépendant - UdeM
Co-superviseur⋅e :
Stagiaire de recherche
Doctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Doctorat - UdeM
Co-superviseur⋅e :
Maîtrise recherche - UdeM
Collaborateur·rice de recherche - UdeM
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Doctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Collaborateur·rice alumni - UdeM
Co-superviseur⋅e :
Collaborateur·rice alumni - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche
Maîtrise recherche - Concordia
Superviseur⋅e principal⋅e :
Maîtrise recherche - UdeM
Maîtrise professionnelle - UdeM
Doctorat - Concordia
Superviseur⋅e principal⋅e :
Maîtrise recherche - UdeM
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni
Maîtrise recherche - UdeM
Maîtrise recherche - UdeM
Maîtrise recherche - UdeM
Doctorat - UdeM
Co-superviseur⋅e :
Collaborateur·rice de recherche
Co-superviseur⋅e :
Doctorat - McGill
Superviseur⋅e principal⋅e :
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Collaborateur·rice de recherche - UdeM
Doctorat - UdeM
Doctorat - McGill
Superviseur⋅e principal⋅e :
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - Concordia
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Co-superviseur⋅e :
Maîtrise recherche - UdeM
Maîtrise recherche - UdeM
Maîtrise recherche - UdeM

Publications

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
Arnav Kumar Jain
Harley Wiltzer
Jesse Farebrother
Sanjiban Choudhury
GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models
Nizar Islah
Justine Gehring
Diganta Misra
Eilif Muller
Terry Yue Zhuo
Massimo Caccia
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning
Md Rifat Arefin
Gopeshh Raaj Subbaraj
Nicolas Gontier
Yann LeCun
Ravid Shwartz-Ziv
Context is Key: A Benchmark for Forecasting with Essential Textual Information
Arjun Ashok
Andrew Robert Williams
Étienne Marcotte
Valentina Zantedeschi
Jithendaraa Subramanian
Roland Riachi
James Requeima
Alexandre Lacoste
Introducing Brain Foundation Models
Mohammad Javad Darvishi Bayazi
Hena Ghonia
Roland Riachi
Bruno Aristimunha
Arian Khorasani
Md Rifat Arefin
Amin Darabi
Sylvain Chevallier
Brain function represents one of the most complex systems driving our world. Decoding its signals poses significant challenges, particularly… (voir plus) due to the limited availability of data and the high cost of recordings. The existence of large hospital datasets and laboratory collections partially mitigates this issue. However, the lack of standardized recording protocols, varying numbers of channels, diverse setups, scenarios, and recording devices further complicate the task. This work addresses these challenges by introducing the Brain Foundation Model (BFM), a suite of open-source models trained on brain signals. These models serve as foundational tools for various types of time-series neuroimaging tasks. This work presents the first model of the BFM series, which is trained on electroencephalogram signal data. Our results demonstrate that BFM-EEG can generate signals more accurately than other models. Upon acceptance, we will release the model weights and pipeline.
Language model scaling laws and zero-sum learning
Andrei Mircea
Ekaterina Lobacheva
Supriyo Chakraborty
Nima Chitsazan
This work aims to understand how, in terms of training dynamics, scaling up language model size yields predictable loss improvements. We fin… (voir plus)d that these improvements can be tied back to loss deceleration, an abrupt transition in the rate of loss improvement, characterized by piece-wise linear behavior in log-log space. Notably, improvements from increased model size appear to be a result of (1) improving the loss at which this transition occurs; and (2) improving the rate of loss improvement after this transition. As an explanation for the mechanism underlying this transition (and the effect of model size on loss it mediates), we propose the zero-sum learning (ZSL) hypothesis. In ZSL, per-token gradients become systematically opposed, leading to degenerate training dynamics where the model can't improve loss on one token without harming it on another; bottlenecking the overall rate at which loss can improve. We find compelling evidence of ZSL, as well as unexpected results which shed light on other factors contributing to ZSL.
LLMs and Personalities: Inconsistencies Across Scales
Tosato Tommaso
Mahmood Hegazy
David Lemay
Mohammed Abukalam
This study investigates the application of human psychometric assessments to large language models (LLMs) to examine their consistency and m… (voir plus)alleability in exhibiting personality traits. We administered the Big Five Inventory (BFI) and the Eysenck Personality Questionnaire-Revised (EPQ-R) to various LLMs across different model sizes and persona prompts. Our results reveal substantial variability in responses due to question order shuffling, challenging the notion of a stable LLM "personality." Larger models demonstrated more consistent responses, while persona prompts significantly influenced trait scores. Notably, the assistant persona led to more predictable scaling, with larger models exhibiting more socially desirable and less variable traits. In contrast, non-conventional personas displayed unpredictable behaviors, sometimes extending personality trait scores beyond the typical human range. These findings have important implications for understanding LLM behavior under different conditions and reflect on the consequences of scaling.
LLMs and Personalities: Inconsistencies Across Scales
Tosato Tommaso
Mahmood Hegazy
David Lemay
Mohammed Abukalam
This study investigates the application of human psychometric assessments to large language models (LLMs) to examine their consistency and m… (voir plus)alleability in exhibiting personality traits. We administered the Big Five Inventory (BFI) and the Eysenck Personality Questionnaire-Revised (EPQ-R) to various LLMs across different model sizes and persona prompts. Our results reveal substantial variability in responses due to question order shuffling, challenging the notion of a stable LLM "personality." Larger models demonstrated more consistent responses, while persona prompts significantly influenced trait scores. Notably, the assistant persona led to more predictable scaling, with larger models exhibiting more socially desirable and less variable traits. In contrast, non-conventional personas displayed unpredictable behaviors, sometimes extending personality trait scores beyond the typical human range. These findings have important implications for understanding LLM behavior under different conditions and reflect on the consequences of scaling.
RedPajama: an Open Dataset for Training Large Language Models
Maurice Weber
Daniel Y Fu
Quentin Gregory Anthony
Yonatan Oren
Shane Adams
Anton Alexandrov
Xiaozhong Lyu
Huu Nguyen
Xiaozhe Yao
Virginia Adams
Ben Athiwaratkun
Rahul Chalamala
Kezhen Chen
Max Ryabinin
Tri Dao
Percy Liang
Christopher Re
Ce Zhang
Using Unity to Help Solve Reinforcement Learning
Connor Brennan
Andrew Robert Williams
Omar G. Younis
Vedant Vyas
Daria Yasafova
Leveraging the depth and flexibility of XLand as well as the rapid prototyping features of the Unity engine, we present the United Unity Uni… (voir plus)verse — an open-source toolkit designed to accelerate the creation of innovative reinforcement learning environments. This toolkit includes a robust implementation of XLand 2.0 complemented by a user-friendly interface which allows users to modify the details of procedurally generated terrains and task rules with ease. Additionally, we provide a curated selection of terrains and rule sets, accompanied by implementations of reinforcement learning baselines to facilitate quick experimentation with novel architectural designs for adaptive agents. Furthermore, we illustrate how the United Unity Universe serves as a high-level language that enables researchers to develop diverse and endlessly variable 3D environments within a unified framework. This functionality establishes the United Unity Universe (U3) as an essential tool for advancing the field of reinforcement learning, especially in the development of adaptive and generalizable learning systems.
When Machines Outshine Humans in Object Recognition, Benchmarking Dilemma
Mohammad Javad Darvishi Bayazi
Md Rifat Arefin
Jocelyn Faubert
Knowledge Distillation in Federated Learning: A Practical Guide
Alessio Mora
Irene Tenison
Paolo Bellavista