Guy Wolf

Nader Asadi

Collaborateur·rice alumni

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Doctorat - UdeM

semihcanturk00@gmail.com

Collaborateur·rice alumni

Google Scholar

Kameron Harris

Collaborateur·rice de recherche - Western Washington University (faculty; assistant prof))

Co-superviseur⋅e :

Doctorat - UdeM

Will Hua

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Paul Janson

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Charles-Etienne Joseph

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Jake Kovalic

Collaborateur·rice de recherche - Yale

Postdoctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Paul François

Paria Mehrbod

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Lydia Mezrag

Doctorat - UdeM

Sacha Morin

Doctorat - UdeM

Co-superviseur⋅e :

Liam Paull

Hasti Nafisi

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Yashar Hezaveh

Geraldin Nanfack

Postdoctorat - Concordia

Superviseur⋅e principal⋅e :

Amine Natik

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Lajoie

Shuang Ni

Doctorat - UdeM

Site web

stephanie.zandee@mcgill.ca

Albert Orozco Camacho

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Visiteur de recherche indépendant

Maîtrise recherche - UdeM

Donald Shenaj

Collaborateur·rice de recherche - Concordia

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - UdeM

Co-superviseur⋅e :

Collaborateur·rice de recherche - Yale

Doctorat - UdeM

Stagiaire de recherche - Western Washington University

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Collaborateur·rice de recherche - McGill (assistant professor)

Site web

Google Scholar

Publications

AdaFisher: Adaptive Second Order Optimization via Fisher Information

Damien Martins Gomes

Yanlei Zhang

Mahdi S. Hosseini

First-order optimization methods are currently the mainstream in training deep neural networks (DNNs). Optimizers like Adam incorporate limi… (voir plus)ted curvature information by employing the diagonal matrix preconditioning of the stochastic gradient during the training. Despite their widespread, second-order optimization algorithms exhibit superior convergence properties compared to their first-order counterparts e.g. Adam and SGD. However, their practicality in training DNNs are still limited due to increased per-iteration computations and suboptimal accuracy compared to the first order methods. We present AdaFisher--an adaptive second-order optimizer that leverages a block-diagonal approximation to the Fisher information matrix for adaptive gradient preconditioning. AdaFisher aims to bridge the gap between enhanced convergence capabilities and computational efficiency in second-order optimization framework for training DNNs. Despite the slow pace of second-order optimizers, we showcase that AdaFisher can be reliably adopted for image classification, language modelling and stand out for its stability and robustness in hyperparameter tuning. We demonstrate that AdaFisher outperforms the SOTA optimizers in terms of both accuracy and convergence speed. Code available from \href{https://github.com/AtlasAnalyticsLab/AdaFisher}{https://github.com/AtlasAnalyticsLab/AdaFisher}

2024-05-26

ArXiv (prépublication)

arxiv.org

Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression

Liam Hodgson

Yue Li

Yasser Iturria-Medina

Jo Anne Stratton

Smita Krishnaswamy

David A. Bennett

Danilo Bzdok

2024-05-17

Communications Biology (publié)

Sustained IFN signaling is associated with delayed development of SARS-CoV-2-specific immunity

Elsa Brunet-Ratnasingham

Sacha Morin

Haley E. Randolph

Marjorie Labrecque

Justin Bélair

Raphaël Lima-Barbosa

Amélie Pagliuzza

Lorie Marchitto

Michael Hultström

Julia Niessl

Rose Cloutier

Alina M. Sreng Flores

Nathalie Brassard

Mehdi Benlarbi

Jérémie Prévost

Shilei Ding

Sai Priya Anand

Gérémy Sannier

Amanda Marks

Dick Wågsäter … (voir 27 de plus)

Eric Bareke

Hugo Zeberg

Miklos Lipcsey

Robert Frithiof

Anders Larsson

Sirui Zhou

Tomoko Nakanishi

David R. Morrison

Dani Vezina

Catherine Bourassa

Gabrielle Gendron-Lepage

Halima Medjahed

Floriane Point

Jonathan Richard

Catherine Larochelle

Alexandre Prat

Janet L. Cunningham

Nathalie Arbour

Madeleine Durand

J. Brent Richards

Kevin R. Moon

Nicolas Chomont

Andrés Finzi

Martine Tétreault

Luis Barreiro

Daniel E. Kaufmann

Plasma RNAemia, delayed antibody responses and inflammation predict COVID-19 outcomes, but the mechanisms underlying these immunovirological… (voir plus) patterns are poorly understood. We profile 782 longitudinal plasma samples from 318 hospitalized COVID-19 patients. Integrated analysis using k-means reveal four patient clusters in a discovery cohort: mechanically ventilated critically-ill cases are subdivided into good prognosis and high-fatality clusters (reproduced in a validation cohort), while non-critical survivors are delineated by high and low antibody responses. Only the high-fatality cluster is enriched for transcriptomic signatures associated with COVID-19 severity, and each cluster has distinct RBD-specific antibody elicitation kinetics. Both critical and non-critical clusters with delayed antibody responses exhibit sustained IFN signatures, which negatively correlate with contemporaneous RBD-specific IgG levels and absolute SARS-CoV-2-specific B and CD4+ T cell frequencies. These data suggest that the Interferon paradox previously described in murine LCMV models is operative in COVID-19, with excessive IFN signaling delaying development of adaptive virus-specific immunity.

2024-05-16

Nature Communications (publié)

Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis

Stefan Horoi

Albert Manuel Orozco Camacho

Ensembling multiple models enhances predictive performance by utilizing the varied learned features of the different models but incurs signi… (voir plus)ficant computational and storage costs. Model fusion, which combines parameters from multiple models into one, aims to mitigate these costs but faces practical challenges due to the complex, non-convex nature of neural network loss landscapes, where learned minima are often separated by high loss barriers. Recent works have explored using permutations to align network features, reducing the loss barrier in parameter space. However, permutations are restrictive since they assume a one-to-one mapping between the different models' neurons exists. We propose a new model merging algorithm, CCA Merge, which is based on Canonical Correlation Analysis and aims to maximize the correlations between linear combinations of the model features. We show that our method of aligning models leads to better performances than past methods when averaging models trained on the same, or differing data splits. We also extend this analysis into the harder many models setting where more than 2 models are merged, and we find that CCA Merge works significantly better in this setting than past methods.

2024-05-01

ICML.cc/2024/Conference (poster)

Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport

Alexander Tong

Nikolay Malkin

Guillaume Huguet

Yanlei Zhang

Jarrid Rector-Brooks

Kilian FATRAS

Yoshua Bengio

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 \textit{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, OT-CFM is the first method to compute dynamic OT in a simulation-free way. 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 Schrödinger bridge inference.

2024-03-11

TMLR (accepté)

Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis

Stefan Horoi

Albert Manuel Orozco Camacho

2024-03-02

ICLR.cc/2024/Workshop/Re-Align (poster)

Learning and Aligning Structured Random Feature Networks

Vivian White

Muawiz Sajjad Chaudhary

Guillaume Lajoie

Kameron Decker Harris

Artificial neural networks (ANNs) are considered "black boxes'' due to the difficulty of interpreting their learned weights. While choosing… (voir plus) the best features is not well understood, random feature networks (RFNs) and wavelet scattering ground some ANN learning mechanisms in function space with tractable mathematics. Meanwhile, the genetic code has evolved over millions of years, shaping the brain to develop variable neural circuits with reliable structure that resemble RFNs. We explore a similar approach, embedding neuro-inspired, wavelet-like weights into multilayer RFNs. These can outperform scattering and have kernels that describe their function space at large width. We build learnable and deeper versions of these models where we can optimize separate spatial and channel covariances of the convolutional weight distributions. We find that these networks can perform comparatively with conventional ANNs while dramatically reducing the number of trainable parameters. Channel covariances are most influential, and both weight and activation alignment are needed for classification performance. Our work outlines how neuro-inspired configurations may lead to better performance in key cases and offers a potentially tractable reduced model for ANN learning.

2024-03-02

ICLR.cc/2024/Workshop/Re-Align (poster)

Learning and Aligning Structured Random Feature Networks

Vivian White

Muawiz Sajjad Chaudhary

Guillaume Lajoie

Kameron Decker Harris

Artificial neural networks (ANNs) are considered ``black boxes'' due to the difficulty of interpreting their learned weights. While choosin… (voir plus)g the best features is not well understood, random feature networks (RFNs) and wavelet scattering ground some ANN learning mechanisms in function space with tractable mathematics. Meanwhile, the genetic code has evolved over millions of years, shaping the brain to devlop variable neural circuits with reliable structure that resemble RFNs. We explore a similar approach, embedding neuro-inspired, wavelet-like weights into multilayer RFNs. These can outperform scattering and have kernels that describe their function space at large width. We build learnable and deeper versions of these models where we can optimize separate spatial and channel covariances of the convolutional weight distributions. We find that these networks can perform comparatively with conventional ANNs while dramatically reducing the number of trainable parameters. Channel covariances are most influential, and both weight and activation alignment are needed for classification performance. Our work outlines how neuro-inspired configurations may lead to better performance in key cases and offers a potentially tractable reduced model for ANN learning.

2024-03-02

ICLR.cc/2024/Workshop/Re-Align (poster)

Generalization of deep learning models for hepatic steatosis grading using B-mode ultrasound images

Pedro Vianna

Yue Qi

Michael Chassé

An Tang

Guy Cloutier

2024-03-01

The Journal of the Acoustical Society of America (publié)

Channel-Selective Normalization for Label-Shift Robust Test-Time Adaptation

Pedro Vianna

Muawiz Chaudhary

Paria Mehrbod

An Tang

Guy Cloutier

Michael Eickenberg

Deep neural networks have useful applications in many different tasks, however their performance can be severely affected by changes in the … (voir plus)data distribution. For example, in the biomedical field, their performance can be affected by changes in the data (different machines, populations) between training and test datasets. To ensure robustness and generalization to real-world scenarios, test-time adaptation has been recently studied as an approach to adjust models 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. It is implemented by recalculating batch normalization statistics on test batches. Prior work has focused on analysis with test data that has the same label distribution as the training data. However, in many practical applications this technique is vulnerable to label distribution shifts, sometimes producing catastrophic failure. This presents a risk in applying test time adaptation methods in deployment. We propose to tackle this challenge by only selectively adapting channels in a deep network, minimizing drastic adaptation that is sensitive to label shifts. Our selection scheme is based on two principles that we empirically motivate: (1) later layers of networks are more sensitive to label shift (2) individual features can be sensitive to specific classes. We apply the proposed technique to three classification tasks, including CIFAR10-C, Imagenet-C, and diagnosis of fatty liver, where we explore both covariate and label distribution shifts. We find that our method allows to bring the benefits of TTA while significantly reducing the risk of failure common in other methods, while being robust to choice in hyperparameters.

2024-02-07

ArXiv (prépublication)

arxiv.org

Effective Protein-Protein Interaction Exploration with PPIretrieval

Chenqing Hua

Connor Coley

Doina Precup

Shuangjia Zheng

2024-02-06

ArXiv (prépublication)

arxiv.org

Gaining Biological Insights through Supervised Data Visualization

Jake S. Rhodes

Adrien Aumon

Sacha Morin

Marc Girard

Catherine Larochelle

Boaz Lahav

Elsa Brunet-Ratnasingham

Amélie Pagliuzza

Lorie Marchitto

Wei Zhang

Adele Cutler

F. Grand'Maison

Anhong Zhou

Andrés Finzi

Nicolas Chomont

Daniel E. Kaufmann

Stephanie Zandee

Alexandre Prat

Kevin R. Moon

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHAT… (voir plus)E, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE’s prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.

2024-01-21

bioRxiv (prépublication)