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 :

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

Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages

Fatima Mostefai

Isabel Gamache

Arnaud N’Guessan

Justin Pelletier

Jessie Huang

Carmen Lia Murall

Ahmad Pesaranghader

Vanda Gaonac'h-Lovejoy

David J. Hamelin

Raphael Poujol

Jean-Christophe Grenier

Martin Smith

Etienne Caron

Morgan Craig

Smita Krishnaswamy

B. Jesse Shapiro

2022-02-21

Frontiers in Medicine (publié)

Goal-driven optimization of single-neuron properties in artiﬁcial networks reveals regularization role of neural diversity and adaptation in the brain

Victor Geadah

Stefan Horoi

Giancarlo Kerg

Neurons in the brain have rich and adaptive input-output properties. Features such as diverse f-I curves and spike frequency adaptation are … (voir plus)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 of 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 plays an active regularization role that enables neural circuits to optimally propagate information across time.

2022-01-01

(publié)

www.semanticscholar.org

Goal-driven optimization of single-neuron properties in artiﬁcial networks reveals regularization role of neural diversity and adaptation in the brain

Victor Geadah

Stefan Horoi

Giancarlo Kerg

2022-01-01

(published)

www.semanticscholar.org

Long Range Graph Benchmark

Vijay Prakash Dwivedi

Ladislav Rampášek

Mikhail Galkin

Ali Parviz

Anh Tuan Luu

Dominique Beaini

Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to b… (voir plus)uild node representations at each layer. In principle, such networks are not able to capture long-range interactions (LRI) that may be desired or necessary for learning a given task on graphs. Recently, there has been an increasing interest in development of Transformer-based methods for graphs that can consider full node connectivity beyond the original sparse structure, thus enabling the modeling of LRI. However, MP-GNNs that simply rely on 1-hop message passing often fare better in several existing graph benchmarks when combined with positional feature representations, among other innovations, hence limiting the perceived utility and ranking of Transformer-like architectures. Here, we present the Long Range Graph Benchmark (LRGB) with 5 graph learning datasets: PascalVOC-SP, COCO-SP, PCQM-Contact, Peptides-func and Peptides-struct that arguably require LRI reasoning to achieve strong performance in a given task. We benchmark both baseline GNNs and Graph Transformer networks to verify that the models which capture long-range dependencies perform significantly better on these tasks. Therefore, these datasets are suitable for benchmarking and exploration of MP-GNNs and Graph Transformer architectures that are intended to capture LRI.

openreview.net

Recipe for a General, Powerful, Scalable Graph Transformer

Ladislav Rampášek

Mikhail Galkin

Vijay Prakash Dwivedi

Anh Tuan Luu

Dominique Beaini

We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art result… (voir plus)s on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph representation learning with a variety of recent publications but they lack a common foundation about what constitutes a good positional or structural encoding, and what differentiates them. In this paper, we summarize the different types of encodings with a clearer definition and categorize them as being

openreview.net

Genomic epidemiology and associated clinical outcomes of a SARS-CoV-2 outbreak in a general adult hospital in Quebec

Bastien Paré

Marieke Rozendaal

Sacha Morin

Léa Kaufmann

Shawn M. Simpson

Raphael Poujol

Fatima Mostefai

Jean-Christophe Grenier

Henry Xing

Miguelle Sanchez

Ariane Yechouron

Ronald Racette

Ivan Pavlov

Martin Smith

2021-12-02

PLoS ONE (publié)

Patient health records and whole viral genomes from an early SARS-CoV-2 outbreak in a Quebec hospital reveal features associated with favorable outcomes

Bastien Paré

Marieke Rozendaal

Sacha Morin

Léa Kaufmann

Shawn M. Simpson

Raphael Poujol

Fatima Mostefai

Jean-Christophe Grenier

Henry Xing

Miguelle Sanchez

Ariane Yechouron

Ronald Racette

Ivan Pavlov

Martin Smith

2021-12-02

PLoS ONE (publié)

Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators

Alexander Tong

Smita Krishnaswamy

Anomaly detection is of great interest in fields where abnormalities need to be identified and corrected (e.g., medicine and finance). Deep … (voir plus)learning methods for this task often rely on autoencoder reconstruction error, sometimes in conjunction with other penalties. We show that this approach exhibits intrinsic biases that lead to undesirable results. Reconstruction-based methods can sometimes show low error on simple-to-reconstruct points that are not part of the training data, for example the all black image. Instead, we introduce a new unsupervised Lipschitz anomaly discriminator (LAD) that does not suffer from these biases. Our anomaly discriminator is trained, similar to the discriminator of a GAN, to detect the difference between the training data and corruptions of the training data. We show that this procedure successfully detects unseen anomalies with guarantees on those that have a certain Wasserstein distance from the data or corrupted training set. These additions allow us to show improved performance on MNIST, CIFAR10, and health record data. Further, LAD does not require decoding back to the original data space, which makes anomaly detection possible in domains where it is difficult to define a decoder, such as in irregular graph structured data. Empirically, we show this framework leads to improved performance on image, health record, and graph data.

2021-11-28

Journal of Signal Processing Systems (publié)

Data-driven approaches for genetic characterization of SARS-CoV-2 lineages

Fatima Mostefai

Isabel Gamache

Jessie Huang

Arnaud N’Guessan

Justin Pelletier

Ahmad Pesaranghader

David J. Hamelin

Carmen Lia Murall

Raphael Poujol

Jean-Christophe Grenier

Martin Smith

Etienne Caron

Morgan Craig

Jesse Shapiro

Smita Krishnaswamy

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19)… (voir plus), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajima’s D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.

2021-09-29

bioRxiv (prépublication)

Parametric Scattering Networks

Shanel Gauthier

Benjamin Th'erien

Laurent Alséne-Racicot

Irina Rish

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.

2021-07-20

ArXiv (preprint)

arxiv.org

Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance

Alexander Tong

Guillaume Huguet

Dennis L. Shung

Amine Natik

Manik Kuchroo

Smita Krishnaswamy

In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (voir plus)s in many domains. Further

2021-01-01

arXiv.org (preprint)

dblp.uni-trier.de

Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance

Alexander Tong

Guillaume Huguet

Dennis Shung

Amine Natik

Manik Kuchroo