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 :

Eugene Belilovsky

geraldin.nanfack@mila.quebec

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 :

Eugene Belilovsky

Lydia Mezrag

Doctorat - UdeM

Kevin Moon

Visiteur de recherche indépendant

Sacha Morin

Doctorat - UdeM

Co-superviseur⋅e :

Postdoctorat - Concordia

Superviseur⋅e principal⋅e :

Eugene Belilovsky

Shuang Ni

Doctorat - UdeM

stephanie.zandee@mcgill.ca

Albert Orozco Camacho

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Matthew Scicluna

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Pedro Vianna

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

Improving and generalizing flow-based generative models with minibatch optimal transport

Alexander Tong

Yanlei Zhang

Kilian FATRAS

Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their si… (voir plus)mulation-based maximum likelihood training. We introduce the generalized conditional flow matching (CFM) technique, a family of simulation-free training objectives for CNFs. CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models. In contrast to both diffusion models and prior CNF training algorithms, CFM does not require the source distribution to be Gaussian or require evaluation of its density. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference, as evaluated in our experiments. Furthermore, we show that when the true OT plan is available, our OT-CFM method approximates dynamic OT. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks, such as inferring single cell dynamics, unsupervised image translation, and Schr\"odinger bridge inference.

2023-02-01

ArXiv (prépublication)

Improving and generalizing flow-based generative models with minibatch optimal transport

Alexander Tong

Yanlei Zhang

Kilian FATRAS

2023-02-01

ArXiv (prépublication)

Improving and generalizing flow-based generative models with minibatch optimal transport

Alexander Tong

Yanlei Zhang

Kilian FATRAS

2023-02-01

ArXiv (prépublication)

Improving and generalizing flow-based generative models with minibatch optimal transport

Alexander Tong

Yanlei Zhang

Kilian FATRAS

2023-02-01

ArXiv (prépublication)

Reliability of CKA as a Similarity Measure in Deep Learning

MohammadReza Davari

Amine Natik

Comparing learned neural representations in neural networks is a challenging but important problem, which has been approached in different w… (voir plus)ays. The Centered Kernel Alignment (CKA) similarity metric, particularly its linear variant, has recently become a popular approach and has been widely used to compare representations of a network's different layers, of architecturally similar networks trained differently, or of models with different architectures trained on the same data. A wide variety of claims about similarity and dissimilarity of these various representations have been made using CKA results. In this work we present analysis that formally characterizes CKA sensitivity to a large class of simple transformations, which can naturally occur in the context of modern machine learning. This provides a concrete explanation to CKA sensitivity to outliers, which has been observed in past works, and to transformations that preserve the linear separability of the data, an important generalization attribute. We empirically investigate several weaknesses of the CKA similarity metric, demonstrating situations in which it gives unexpected or counterintuitive results. Finally we study approaches for modifying representations to maintain functional behaviour while changing the CKA value. Our results illustrate that, in many cases, the CKA value can be easily manipulated without substantial changes to the functional behaviour of the models, and call for caution when leveraging activation alignment metrics.

2023-02-01

ICLR.cc/2023/Conference (poster)

Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport

Alexander Tong

Yanlei Zhang

Kilian FATRAS

Continuous normalizing ﬂows (CNFs) are an attractive generative modeling technique, but they have thus far been held back by limitations i… (voir plus)n their simulation-based maximum likelihood training. In this paper, we introduce a new technique called conditional ﬂow matching (CFM), a simulation-free training objective for CNFs. CFM features a stable regression objective like that used to train the stochastic ﬂow in diffusion models but enjoys the efﬁcient inference of deterministic ﬂow models. In contrast to both diffusion models and prior CNF training algorithms, our CFM objec-tive does not require the source distribution to be Gaussian or require evaluation of its density. Based on this new objective, we also introduce optimal transport CFM (OT-CFM), which creates simpler ﬂows that are more stable to train and lead to faster inference, as evaluated in our experiments. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks such as inferring single cell dynamics, unsupervised image translation, and Schr ¨ odinger bridge inference. Code is available at https://github.com/atong01/ conditional-flow-matching .

2023-01-01

arXiv.org (prépublication)

Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport

Alexander Tong

Yanlei Zhang

Kilian FATRAS

2023-01-01

arXiv.org (prépublication)

GEODESIC SINKHORN FOR FAST AND ACCURATE OPTIMAL TRANSPORT ON MANIFOLDS

Guillaume Huguet

Alexander Tong

María Ramos Zapatero

Christopher J. Tape

Efficient computation of optimal transport distance between distributions is of growing importance in data science. Sinkhorn-based methods a… (voir plus)re currently the state-of-the-art for such computations, but require O(n2) computations. In addition, Sinkhorn-based methods commonly use an Euclidean ground distance between datapoints. However, with the prevalence of manifold structured scientific data, it is often desirable to consider geodesic ground distance. Here, we tackle both issues by proposing Geodesic Sinkhorn—based on diffusing a heat kernel on a manifold graph. Notably, Geodesic Sinkhorn requires only O(n log n) computation, as we approximate the heat kernel with Chebyshev polynomials based on the sparse graph Laplacian. We apply our method to the computation of barycenters of several distributions of high dimensional single cell data from patient samples undergoing chemotherapy. In particular, we define the barycentric distance as the distance between two such barycenters. Using this definition, we identify an optimal transport distance and path associated with the effect of treatment on cellular data.

2023-01-01

2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) (publié)

A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction

Guillaume Huguet

Alexander Tong

Edward De Brouwer

Yanlei Zhang

Ian Adelstein

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

2023-01-01

LoG (publié)

Learning Shared Neural Manifolds from Multi-Subject FMRI Data

Jessie Huang

Je-chun Huang

Erica Lindsey Busch

Tom Wallenstein

Michal Gerasimiuk

Andrew Benz

Guillaume Lajoie

Nicholas Turk-Browne

Functional magnetic resonance imaging (fMRI) data is collected in millions of noisy, redundant dimensions. To understand how different brain… (voir plus)s process the same stimulus, we aim to denoise the fMRI signal via a meaningful embedding space that captures the data's intrinsic structure as shared across brains. We assume that stimulus-driven responses share latent features common across subjects that are jointly discoverable. Previous approaches to this problem have relied on linear methods like principal component analysis and shared response modeling. We propose a neural network called MRMD-AE (manifold-regularized multiple- decoder, autoencoder) that learns a common embedding from multi-subject fMRI data while retaining the ability to decode individual responses. Our latent common space represents an extensible manifold (where untrained data can be mapped) and improves classification accuracy of stimulus features of unseen timepoints, as well as cross-subject translation of fMRI signals.

2022-08-22

2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP) (publié)

Parametric Scattering Networks

Shanel Gauthier

Benjamin Therien

Laurent Alséne-Racicot

Michael Eickenberg

The wavelet scattering transform creates geometric in-variants and deformation stability. In multiple signal do-mains, it has been shown to … (voir plus)yield more discriminative rep-resentations compared to other non-learned representations and to outperform learned representations in certain tasks, particularly on limited labeled data and highly structured signals. The wavelet filters used in the scattering trans-form are typically selected to create a tight frame via a pa-rameterized mother wavelet. In this work, we investigate whether this standard wavelet filterbank construction is op-timal. Focusing on Morlet wavelets, we propose to learn the scales, orientations, and aspect ratios of the filters to produce problem-specific parameterizations of the scattering transform. We show that our learned versions of the scattering transform yield significant performance gains in small-sample classification settings over the standard scat-tering transform. Moreover, our empirical results suggest that traditional filterbank constructions may not always be necessary for scattering transforms to extract effective rep-resentations.

2022-06-18

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)