Irina Rish

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

George Adamopoulos

Stagiaire de recherche

Ivan Anokhin

Doctorat - UdeM

Co-superviseur⋅e :

Samira Ebrahimi Kahou

Doctorat - UdeM

Arjun Ashok

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Doctorat - McGill

Superviseur⋅e principal⋅e :

Blake Richards

Mohammad Javad Darvishi Bayazi

Amin Darabi

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Karim Jerbi

Wagner Drew

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Collaborateur·rice alumni - UdeM

Maîtrise recherche

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Ioannis Mitliagkas

Nizar Islah

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Eilif Benjamin Muller

Doctorat - UdeM

Collaborateur·rice de recherche

Zafir Khalid

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Neeraj Kumar

Maîtrise professionnelle - UdeM

Gwen Legate

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Eugene Belilovsky

David Lemay

Maîtrise recherche - UdeM

amin.mansouri@mila.quebec

Jonathan Lim

Collaborateur·rice de recherche

Maîtrise recherche - UdeM

Collaborateur·rice alumni

Doctorat - UdeM

Maîtrise recherche - UdeM

Diganta Misra

Maîtrise recherche - UdeM

Timothy Nest

Doctorat - UdeM

Co-superviseur⋅e :

Eilif Benjamin Muller

Mohammad Pezeshki

Collaborateur·rice de recherche

Co-superviseur⋅e :

Doctorat - McGill

Superviseur⋅e principal⋅e :

Pouya Bashivan

Mahta Ramezanian

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Guillaume Dumas

Roland Riachi

Collaborateur·rice de recherche - UdeM

Matthew Riemer

Doctorat - UdeM

Alexis Roger

Doctorat - McGill

Superviseur⋅e principal⋅e :

Blake Richards

Zahra Sheikhbahaee

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Dumas

Vaibhav Singh

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Publications

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.

2024-09-26

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)

When Machines Outshine Humans in Object Recognition, Benchmarking Dilemma

Mohammad Javad Darvishi Bayazi

Md Rifat Arefin

Jocelyn Faubert

2024-09-15

Journal of Vision (publié)

Knowledge Distillation in Federated Learning: A Practical Guide

Alessio Mora

Irene Tenison

Paolo Bellavista

2024-08-01

Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence (publié)

Spectra: A Comprehensive Study of Ternary, Quantized, and FP16 Language Models

Ayush Kaushal

Tejas Pandey

Tejas Vaidhya

Aaryan Bhagat

2024-07-17

ArXiv (prépublication)

arxiv.org

Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale

Ayush Kaushal

Tejas Pandey

Tejas Vaidhya

Aaryan Bhagat

2024-07-17

ArXiv (prépublication)

arxiv.org

Simple and Scalable Strategies to Continually Pre-train Large Language Models

Adam Ibrahim

Benjamin Thérien

Kshitij Gupta

Mats Leon Richter

Quentin Gregory Anthony

Timothee LESORT

Eugene Belilovsky

2024-07-08

TMLR (accepté)

Unsupervised Concept Discovery Mitigates Spurious Correlations

Md Rifat Arefin

Yan Zhang

Aristide Baratin

Francesco Locatello

Dianbo Liu

Kenji Kawaguchi

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression

Ayush Kaushal

Tejas Vaidhya

Low Rank Decomposition of matrix - splitting a large matrix into a product of two smaller matrix offers a means for compression that reduces… (voir plus) the parameters of a model without sparsification, and hence delivering more speedup on modern hardware. Moreover, unlike quantization, the compressed linear layers remain fully differentiable and all the parameters trainable, while being able to leverage the existing highly efficient kernels over floating point matrices. We study the potential to compress Large Language Models (LLMs) for monolingual Code generation via Low Rank Decomposition (LoRD) and observe that ranks for the linear layers in these models can be reduced by upto 39.58% with less than 1% increase in perplexity. We then use Low Rank Decomposition (LoRD) to compress StarCoder 16B to 13.2B parameter with no drop and to 12.3B with minimal drop in HumanEval Pass@1 score, in less than 10 minutes on a single A100. The compressed models speeds up inference by up to 22.35% with just a single line of change in code over huggingface's implementation with pytorch backend. Low Rank Decomposition (LoRD) models remain compatible with state of the art near-lossless quantization method such as SpQR, which allows leveraging further compression gains of quantization. Lastly, QLoRA over Low Rank Decomposition (LoRD) model further reduces memory requirements by as much as 21.2% over vanilla QLoRA while offering similar gains from parameter efficient fine tuning. Our work shows Low Rank Decomposition (LoRD) as a promising new paradigm for LLM compression.

2024-07-03

ICML.cc/2024/Workshop/FM-Wild (poster)

The Effect of Data Corruption on Multimodal Long Form Responses

Daniel Z Kaplan

Alexis Roger

Mohamed Osman

Despite significant progress, Vision-Language Models (VLMs) still struggle with hallucinations, especially in long-form responses. Existing … (voir plus)strategies have had limited successes in specific cases, and long-form generation remains problematic. In this work we attempt to establish the link between the data used to train the model and the hallucinations in the model's output. To this end, we examine hallucinations through data corruption. We develop a method to corrupt training data and then train models with this data to see the effect on performance. We will show that corrupting only a small portion of the long-form training data significantly impairs the performance of the model on long-form tasks, while leaving simpler tasks like visual question-answering and multiple choice relatively intact. All training code and models are released for reproducibility and future research.

2024-07-03

ICML.cc/2024/Workshop/FM-Wild (poster)

TriLM vs FloatLM: Ternary LLMs are more Performant than Quantized FP16 LLMs

Ayush Kaushal

Tejas Vaidhya

Tejas Pandey

Aaryan Bhagat

Ternary LLMs offer significantly better performance for their size (measured in bits) than the models trained and deployed in FP16/BF16. Giv… (voir plus)en the widespread usage of quantization before deployment and advancements in Post Training Quantization of LLMs, a pivotal question arises: do ternary LLMs indeed provide any discernible benefits? To address this, we first build an open family of pre-trained ternary Large Language Models (TriLM). Additionally, we include their counterparts pre-trained in FP16 (FloatLM) and quantized versions of FloatLM (QuantLM) with parameters across almost two orders of magnitude - from 99M to 3.9B parameters. We demonstrate that TriLMs with 3B+ parameters start to offer competitive performance compared to FloatLMs with the same parameter count, while providing significantly better performance for their size. Specifically, TriLM 3.9B, with less bits than FloatLM 830M, ranks between FloatLM 2.4B and FloatLM 3.9B when averaged across 6 popular commonsense and reasoning benchmarks. TriLMs also outperform quantized models, with TriLM 3.9B surpassing the larger QuantLM-3bit 3.9B. Furthermore, across knowledge-based benchmarks, TriLM maintains a superiority for its size, but lags for its parameter count. TriLM 3.9B falls halfway between FloatLM 1.5B and 2.4B, close to QuantLM-4bit 2.4B. To advance research on Ternary LMs, we open source over 500+ checkpoints across the model families.

2024-07-03

ICML.cc/2024/Workshop/FM-Wild (poster)

VFA: Vision Frequency Analysis of Foundation Models and Human

Mohammad Javad Darvishi Bayazi

Md Rifat Arefin

Jocelyn Faubert

Machine learning models often struggle with distribution shifts in real-world scenarios, whereas humans exhibit robust adaptation. Models th… (voir plus)at better align with human perception may achieve higher out-of-distribution generalization. In this study, we investigate how various characteristics of large-scale computer vision models influence their alignment with human capabilities and robustness. Our findings indicate that increasing model and data size, along with incorporating rich semantic information and multiple modalities, significantly enhances models' alignment with human perception and their overall robustness. Our empirical analysis demonstrates a strong correlation between out-of-distribution accuracy and human alignment.

2024-07-03

ICML.cc/2024/Workshop/FM-Wild (poster)

VFA: Vision Frequency Analysis of Foundation Models and Human

Mohammad Javad Darvishi Bayazi

Md Rifat Arefin

Jocelyn Faubert

2024-07-03

ICML.cc/2024/Workshop/FM-Wild (poster)