Guy Wolf

Membre académique principal

wolfguy@mila.quebec

Chaire en IA Canada-CIFAR

Professeur agrégé, Université de Montréal, Département de mathématiques et statistiques

Concordia University

CHUM - Montreal University Hospital Center

Site web

Google Scholar

Biographie

Guy Wolf est professeur agrégé au Département de mathématiques et de statistique de l'Université de Montréal. Ses intérêts de recherche se situent au carrefour de l'apprentissage automatique, de la science des données et des mathématiques appliquées. Il s'intéresse particulièrement aux méthodes d'exploration de données qui utilisent l'apprentissage multiple et l'apprentissage géométrique profond, ainsi qu'aux applications pour l'analyse exploratoire des données biomédicales.

Ses recherches portent sur l'analyse exploratoire des données, avec des applications en bio-informatique. Ses approches sont multidisciplinaires et combinent l'apprentissage automatique, le traitement du signal et les outils mathématiques appliqués. En particulier, ses travaux récents utilisent une combinaison de géométries de diffusion et d'apprentissage profond pour trouver des modèles émergents, des dynamiques et des structures dans les mégadonnées à grande dimension (par exemple, dans la génomique et la protéomique de la cellule unique).

Étudiants actuels

Adrien Aumon

Doctorat - Université de Montréal

adrien.aumon@mila.quebec

Albert Orozco Camacho

Doctorat - Concordia University

Superviseur⋅e principal⋅e :

Amine Natik

Doctorat - Université de Montréal

Superviseur⋅e principal⋅e :

Guillaume Lajoie

natikami@mila.quebec

Charles-Etienne Joseph

Maîtrise recherche - Université de Montréal

Superviseur⋅e principal⋅e :

Eugene Belilovsky

charles-etienne.joseph@mila.quebec

Github

Will Hua

Maîtrise recherche - McGill University

Superviseur⋅e principal⋅e :

Doina Precup

chenqing.hua@mila.quebec

Donald Shenaj

Collaborateur·rice de recherche - Concordia University

Superviseur⋅e principal⋅e :

Eugene Belilovsky

donald.shenaj@mila.quebec

Visiteur de recherche indépendant

edouard.oyallon@mila.quebec

Postdoctorat - Concordia University

Superviseur⋅e principal⋅e :

Eugene Belilovsky

geraldin.nanfack@mila.quebec

Guillaume Huguet

Doctorat - Université de Montréal

guillaume.huguet@mila.quebec

Github

Google Scholar

Hasti Nafisi

Maîtrise recherche - Université de Montréal

Co-superviseur⋅e :

Yashar Hezaveh

hasti.nafisi@mila.quebec

Jake Kovalic

Collaborateur·rice de recherche - Yale

jake.kovalic@mila.quebec

Github

Frederik Wenkel

Doctorat - Université de Montréal

frederik.wenkel@mila.quebec

Doctorat - Université de Montréal

lydia.mezrag@mila.quebec

Mehrbod Paria

Maîtrise recherche - Concordia University

Superviseur⋅e principal⋅e :

Eugene Belilovsky

paria.mehrbod@mila.quebec

Muawiz Chaudhary

Collaborateur·rice alumni

muawiz.chaudhary@mila.quebec

Github

Google Scholar

Myriam Lizotte

Doctorat - Université de Montréal

myriam.lizotte@mila.quebec

Nader Asadi

Collaborateur·rice alumni

Superviseur⋅e principal⋅e :

Eugene Belilovsky

nader.asadi@mila.quebec

Collaborateur·rice de recherche - Université de Montréal

Co-superviseur⋅e :

Eugene Belilovsky

pedro.vianna@mila.quebec

Github

Google Scholar

Ria Arora

Maîtrise recherche - Université de Montréal

Co-superviseur⋅e :

Liam Paull

ria.arora@mila.quebec

Github

Sacha Morin

Doctorat - Université de Montréal

Co-superviseur⋅e :

Liam Paull

sacha.morin@mila.quebec

Semih Cantürk

Doctorat - Université de Montréal

semih.canturk@mila.quebec

Site web

Github

Google Scholar

Shuang Ni

Doctorat - Université de Montréal

shuang.ni@mila.quebec

Site web

Github

Siddharth Viswanath

Collaborateur·rice de recherche - Yale

siddarth.viswanath@mila.quebec

Stefan Horoi

Doctorat - Université de Montréal

stefan.horoi@mila.quebec

Postdoctorat - Université de Montréal

letournv@mila.quebec

Vivian White

Stagiaire de recherche - Western Washington University

Superviseur⋅e principal⋅e :

Guillaume Lajoie

vivian.white@mila.quebec

Github

Zhang Yanlei

Postdoctorat - Université de Montréal

yanlei.zhang@mila.quebec

Site web

Google Scholar

Publications

Spectral Temporal Contrastive Learning

Sacha Morin

Somjit Nath

Samira Ebrahimi Kahou

Guy Wolf

Learning useful data representations without requiring labels is a cornerstone of modern deep learning. Self-supervised learning methods, pa… (voir plus)rticularly contrastive learning (CL), have proven successful by leveraging data augmentations to define positive pairs. This success has prompted a number of theoretical studies to better understand CL and investigate theoretical bounds for downstream linear probing tasks. This work is concerned with the temporal contrastive learning (TCL) setting where the sequential structure of the data is used instead to define positive pairs, which is more commonly used in RL and robotics contexts. In this paper, we adapt recent work on Spectral CL to formulate Spectral Temporal Contrastive Learning (STCL). We discuss a population loss based on a state graph derived from a time-homogeneous reversible Markov chain with uniform stationary distribution. The STCL loss enables to connect the linear probing performance to the spectral properties of the graph, and can be estimated by considering previously observed data sequences as an ensemble of MCMC chains.

2023-12-01

ArXiv (prépublication)

doi.org

arxiv.org

Inferring dynamic regulatory interaction graphs from time series data with perturbations

Dhananjay Bhaskar

Daniel Sumner Magruder

Edward De Brouwer

Matheo Morales

Aarthi Venkat

Frederik Wenkel

Guy Wolf

Smita Krishnaswamy

2023-11-18

logconference.io/LOG/2023/Conference (poster)

doi.org

openreview.net

Channel Selection for Test-Time Adaptation Under Distribution Shift

Pedro Vianna

Muawiz Sajjad Chaudhary

An Tang

Guy Cloutier

Guy Wolf

Michael Eickenberg

Eugene Belilovsky

To ensure robustness and generalization to real-world scenarios, test-time adaptation has been recently studied as an approach to adjust mod… (voir plus)els to a new data distribution during inference. Test-time batch normalization is a simple and popular method that achieved compelling performance on domain shift benchmarks by recalculating batch normalization statistics on test batches. However, in many practical applications this technique is vulnerable to label distribution shifts. We propose to tackle this challenge by only selectively adapting channels in a deep network, minimizing drastic adaptation that is sensitive to label shifts. We find that adapted models significantly improve the performance compared to the baseline models and counteract unknown label shifts.

2023-10-27

NeurIPS.cc/2023/Workshop/DistShift (poster)

openreview.net

Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms

Michael Perlmutter

Alexander Tong

Feng Gao

Guy Wolf

Matthew Hirn

The scattering transform is a multilayered wavelet-based deep learning architecture that acts as a model of convolutional neural networks. R… (voir plus)ecently, several works have introduced generalizations of the scattering transform for non-Euclidean settings such as graphs. Our work builds upon these constructions by introducing windowed and non-windowed geometric scattering transforms for graphs based upon a very general class of asymmetric wavelets. We show that these asymmetric graph scattering transforms have many of the same theoretical guarantees as their symmetric counterparts. As a result, the proposed construction unifies and extends known theoretical results for many of the existing graph scattering architectures. In doing so, this work helps bridge the gap between geometric scattering and other graph neural networks by introducing a large family of networks with provable stability and invariance guarantees. These results lay the groundwork for future deep learning architectures for graph-structured data that have learned filters and also provably have desirable theoretical properties.

2023-10-25

SIAM Journal on Mathematics of Data Science (publié)

doi.org

arxiv.org

Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis.

Pedro Vianna

Sara-Ivana Calce

Pamela Boustros

Cassandra Larocque-Rigney

Laurent Patry-Beaudoin

Yi Hui Luo

Emre Aslan

John Marinos

Talal M. Alamri

Kim-Nhien Vu

Jessica Murphy-Lavallée

Jean-Sébastien Billiard

Emmanuel Montagnon

Hongliang Li

Samuel Kadoury

Bich Nguyen

Shanel Gauthier

Benjamin Thérien

Irina Rish

Eugene Belilovsky … (voir 4 de plus)

Guy Wolf

Michaël Chassé

Guy Cloutier

An Tang

Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose T… (voir plus)o evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of κ statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue.

2023-10-01

Radiology (publié)

doi.org

F66. FROM GENE TO COGNITION: MAPPING THE EFFECTS OF GENOMIC DELETIONS AND DUPLICATIONS ON COGNITIVE ABILITY

Sayeh Kazem

Kuldeep Kumar

Guillaume Huguet

Myriam Lizotte

Thomas Renne

Jakub Kopal

Stefan Horoi

Martineau Jean-Louis

Zohra Saci

Laura Almasy

David C. Glahn

Guy Wolf

Guillaume Dumas

Sébastien Jacquemont

2023-10-01

European Neuropsychopharmacology (publié)

doi.org

Graph topological property recovery with heat and wave dynamics-based features on graphs

Dhananjay Bhaskar

Yanlei Zhang

Charles Xu

Xingzhi Sun

Oluwadamilola Fasina

Guy Wolf

Maximilian Nickel

Michael Perlmutter

Smita Krishnaswamy

2023-09-18

ArXiv (prépublication)

doi.org

arxiv.org

Automated liver segmentation and steatosis grading using deep learning on B-mode ultrasound images

Pedro Vianna

Merve Kulbay

Pamela Boustros

Sara-Ivana Calce

Cassandra Larocque-Rigney

Laurent Patry-Beaudoin

Yi Hui Luo

Muawiz Chaudary

Samuel Kadoury

Bich Nguyen

Emmanuel Montagnon

Eugene Belilovsky

Guy Wolf

Michaël Chassé

An Tang

Guy Cloutier

Early detection of nonalcoholic fatty liver disease (NAFLD) is crucial to avoid further complications. Ultrasound is often used for screenin… (voir plus)g and monitoring of hepatic steatosis, however it is limited by the subjective interpretation of images. Computer assisted diagnosis could aid radiologists to achieve objective grading, and artificial intelligence approaches have been tested across various medical applications. In this study, we evaluated the performance of a two-stage hepatic steatosis detection deep learning framework, with a first step of liver segmentation and a subsequent step of hepatic steatosis classification. We evaluated the models on internal and external datasets, aiming to understand the generalizability of the framework. In the external dataset, our segmentation model achieved a Dice score of 0.92 (95% CI: 0.78, 1.00), and our classification model achieved an area under the receiver operating characteristic curve of 0.84 (95% CI: 0.79, 0.89). Our findings highlight the potential benefits of applying artificial intelligence models in NAFLD assessment.

2023-09-03

IUS (publié)

doi.org

Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization

Victor Geadah

Stefan Horoi

Giancarlo Kerg

Guy Wolf

Guillaume Lajoie

Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptatio… (voir plus)n are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single-neuron flexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and neural circuits must be considered as a complete system. In this work, we use artificial neural networks to systematically investigate single-neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear activation function, parametrized to mimic f-I curves of biological neurons, and to learn adaptation strategies to modify activation functions in real-time during a task. We find that such networks show much-improved robustness to noise and changes in input statistics. Importantly, we find that this procedure recovers precise coding strategies found in biological neurons, such as gain scaling and fractional order differentiation/integration. Using tools from dynamical systems theory, we analyze the role of these emergent single-neuron properties and argue that neural diversity and adaptation play an active regularization role, enabling neural circuits to optimally propagate information across time.

2023-07-19

bioRxiv (prépublication)

doi.org

Neural FIM for learning Fisher information metrics from point cloud data

Oluwadamilola Fasina

Guillaume Huguet

Alexander Tong

Yanlei Zhang

Guy Wolf

Maximilian Nickel

Ian Adelstein

Smita Krishnaswamy

Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the under… (voir plus)lying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM’s utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells).

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (publié)

doi.org

openreview.net

Pretrained Language Models to Solve Graph Tasks in Natural Language

Frederik Wenkel

Guy Wolf

Boris Knyazev

Pretrained large language models (LLMs) are powerful learners in a variety of language tasks. We explore if LLMs can learn from graph-struct… (voir plus)ured data when the graphs are described using natural language. We explore data augmentation and pretraining specific to the graph domain and show that LLMs such as GPT-2 and GPT-3 are promising alternatives to graph neural networks.

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

openreview.net

Simulation-Free Schrödinger Bridges via Score and Flow Matching

Alexander Tong

Nikolay Malkin

Kilian FATRAS

Lazar Atanackovic

Yanlei Zhang

Guillaume Huguet

Guy Wolf

Yoshua Bengio

We present simulation-free score and flow matching ([SF]…

2023-06-19

ICML.cc/2023/Workshop/Frontiers4LCD (publié)

doi.org

openreview.net

La recherche en IA au service du monde réel

Boussole des politiques en IA

Vie étudiante et ressources

Guy Wolf

Biographie

Étudiants actuels

Publications

La recherche en IA au service du monde réel

Boussole des politiques en IA

Vie étudiante et ressources

Mots-clés populaires:

Guy Wolf

Biographie

Étudiants actuels

Publications