Guy Wolf

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

Ria Arora

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - UdeM

semihcanturk00@gmail.com

Collaborateur·rice alumni

Enrique Fita Sanmartin

Collaborateur·rice alumni - UdeM

Doctorat - UdeM

Will Hua

Collaborateur·rice alumni - McGill

Xiaolong Huang

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Paul Janson

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Charles-Etienne Joseph

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

M. Elyes Kanoun

Stagiaire de recherche - UdeM

Vincent Létourneau

Collaborateur·rice alumni - UdeM

Myriam Lizotte

Doctorat - UdeM

Philippe Martin

Doctorat - UdeM

Co-superviseur⋅e :

Paul François

Paria Mehrbod

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Lydia Mezrag

Doctorat - UdeM

Kevin Moon

Visiteur de recherche indépendant

Sacha Morin

Doctorat - UdeM

Co-superviseur⋅e :

Postdoctorat - Concordia

Superviseur⋅e principal⋅e :

geraldin.nanfack@mila.quebec

Shuang Ni

Doctorat - UdeM

Albert Orozco Camacho

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Matthew Scicluna

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

stephanie.zandee@mcgill.ca

Collaborateur·rice de recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Postdoctorat - UdeM

Collaborateur·rice de recherche - McGill (assistant professor)

Analyser le paradoxe des interférons inhérent à la COVID-19 au moyen de la réduction de la dimensionnalité et du regroupement

Billets de blogue

Graph and representation of working methodology, and graph of data on deaths 60 days after onset of symptoms.

19 février 2025

par

Sacha Morin

Elsa Brunet-Ratnasingham

Guy Wolf

Lire l'article

Publications

Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy

Danqi Liao

Chen Liu

Benjamin W Christensen

Alexander Tong

Guillaume Huguet

Maximilian Nickel

Ian Adelstein

Entropy and mutual information in neural networks provide rich information on the learning process, but they have proven difficult to comput… (voir plus)e reliably in high dimensions. Indeed, in noisy and high-dimensional data, traditional estimates in ambient dimensions approach a fixed entropy and are prohibitively hard to compute. To address these issues, we leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. Specifically, we define diffusion spectral entropy (DSE) in neural representations of a dataset as well as diffusion spectral mutual information (DSMI) between different variables representing data. First, we show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data that outperform classic Shannon entropy, nonparametric estimation, and mutual information neural estimation (MINE). We then study the evolution of representations in classification networks with supervised learning, self-supervision, or overfitting. We observe that (1) DSE of neural representations increases during training; (2) DSMI with the class label increases during generalizable learning but stays stagnant during overfitting; (3) DSMI with the input signal shows differing trends: on MNIST it increases, while on CIFAR-10 and STL-10 it decreases. Finally, we show that DSE can be used to guide better network initialization and that DSMI can be used to predict downstream classification accuracy across 962 models on ImageNet.

2024-01-01

CISS (publié)

Enhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension

Shuang Ni

Adrien Aumon

Kevin R. Moon

Jake S. Rhodes

The value of supervised dimensionality reduction lies in its ability to uncover meaningful connections between data features and labels. Com… (voir plus)mon dimensionality reduction methods embed a set of fixed, latent points, but are not capable of generalizing to an unseen test set. In this paper, we provide an out-of-sample extension method for the random forest-based supervised dimensionality reduction method, RF-PHATE, combining information learned from the random forest model with the function-learning capabilities of autoencoders. Through quantitative assessment of various autoencoder architectures, we identify that networks that reconstruct random forest proximities are more robust for the embedding extension problem. Furthermore, by leveraging proximity-based prototypes, we achieve a 40% reduction in training time without compromising extension quality. Our method does not require label information for out-of-sample points, thus serving as a semi-supervised method, and can achieve consistent quality using only 10% of the training data.

2024-01-01

MLSP (publié)

Learnable Filters for Geometric Scattering Modules

Alexander Tong

Frederik Wenkel

Dhananjay Bhaskar

Kincaid MacDonald

Jackson Grady

Michael Perlmutter

2024-01-01

IEEE Transactions on Signal Processing (publié)

Simulation-Free Schrödinger Bridges via Score and Flow Matching

Alexander Tong

Nikolay Malkin

Kilian FATRAS

Lazar Atanackovic

Yanlei Zhang

Guillaume Huguet

Yoshua Bengio

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

2024-01-01

AISTATS (publié)

Spectral Temporal Contrastive Learning

Sacha Morin

Somjit Nath

Samira Ebrahimi Kahou

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)

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

2023-11-18

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

Channel Selection for Test-Time Adaptation Under Distribution Shift

Muawiz Sajjad Chaudhary

An Tang

Guy Cloutier

Michael Eickenberg

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)

Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms

Michael Perlmutter

Alexander Tong

Feng Gao

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)

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

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 Therien

Irina Rish

Eugene Belilovsky … (voir 4 de plus)

Michael 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)

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

Guillaume Dumas

Sébastien Jacquemont

2023-10-01

European Neuropsychopharmacology (publié)

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

Dhananjay Bhaskar

Yanlei Zhang

Charles Xu

Xingzhi Sun

Oluwadamilola Fasina

Maximilian Nickel

Michael Perlmutter

2023-09-18

ArXiv (prépublication)

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

Merve Kulbay

Pamela Boustros

Sara-Ivana Calce

Cassandra Larocque-Rigney

Laurent Patry-Beaudoin

Yi Hui Luo

Muawiz Chaudary

Samuel Kadoury

Bich Nguyen

Emmanuel Montagnon

Michael 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)