Publications

APP: Anytime Progressive Pruning

Tianlong Chen

Zhangyang Wang

With the latest advances in deep learning, several methods have been investigated for optimal learning settings in scenarios where the data … (voir plus)stream is continuous over time. However, training sparse networks in such settings has often been overlooked. In this paper, we explore the problem of training a neural network with a target sparsity in a particular case of online learning: the anytime learning at macroscale paradigm (ALMA). We propose a novel way of progressive pruning, referred to as \textit{Anytime Progressive Pruning} (APP); the proposed approach significantly outperforms the baseline dense and Anytime OSP models across multiple architectures and datasets under short, moderate, and long-sequence training. Our method, for example, shows an improvement in accuracy of

2022-11-16

ACML.org/2022/Workshop/CLL (accepté)

doi.org

openreview.net

The Liver Tumor Segmentation Benchmark (LiTS)

Patrick Bilic

Patrick Christ

Eugene Vorontsov

Hongwei Bran Li

Grzegorz Chlebus

Hao Chen

Qi Dou

Chi-Wing Fu

Xu Han

Gabriel Efrain Humpire Mamani

Pheng Ann Heng

Jürgen Hesser

Samuel Kadoury

Julian Walter Holch

Tomasz Konopczynski

Miao Yue

Chunming Li

X. Li

Jana Lipková

John Lowengrub … (voir 99 de plus)

Michal Marianne Amitai

Hans Meine

J. Moltz

Christopher Pal

Marie Piraud

Ivan Ezhov

Xiaojuan Qi

Fernando Navarro

Jin Qi

Florian Kofler

Markus Rempfler

Johannes C. Paetzold

Karsten Roth

Suprosanna Shit

Andrea Schenk

Xiaobin Hu

Anjany Sekuboyina

Ping Zhou

Christian Hülsemeyer

Marcel Beetz

Jan Kirschke

Florian Ettlinger

Felix Gruen

Benedikt Wiestler

Zhiheng Zhang

Georgios Kaissis

Fabian Lohöfer

Rickmer Braren

J. Holch

Michela Antonelli

Felix Hofmann

Woong Bae

Wieland Sommer

Míriam Bellver

Volker Heinemann

Lei Bi

Colin Jacobs

G. Mamani

Bram van Ginneken

Erik B. Dam

Gabriel Chartrand

An Tang

Michal Drozdzal

Bogdan Georgescu

Avi Ben-Cohen

Xavier Giró-i-Nieto

Eyal Klang

M. Amitai

E. Konen

Hayit Greenspan

Johan Moreau

Jan Hendrik Moltz

Alexandre Hostettler

Christian Igel

Luc Soler

Fabian Isensee

Refael Vivanti

Paul Jäger

Adi Szeskin

Fucang Jia

Naama Lev-Cohain

Krishna Chaitanya Kaluva

Jacob Sosna

Mahendra Khened

Leo Joskowicz

Ildoo Kim

Bjoern Menze

Jae-Hun Kim

Zengming Shen

Sungwoong Kim

Simon Kohl

Avinash Kori

Ganapathy Krishnamurthi

Fan Li

Hongchao Li

Junbo Li

Xiaomeng Li

Jun Ma

Klaus Maier-Hein

Kevis-Kokitsi Maninis

Dorit Merhof

Akshay Pai

Mathias Perslev

Jens Petersen

Jordi Pont-Tuset

Oliver Rippel

Ignacio Sarasua

Jordi Torres

Christian Wachinger

Chunliang Wang

Leon Weninger

Jianrong Wu

Daguang Xu

Xiaoping Yang

Simon Chun-Ho Yu

Yading Yuan

Liping Zhang

Jorge Cardoso

Spyridon Bakas

2022-11-16

Medical image analysis (publié)

doi.org

arxiv.org

Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors

Anjun Hu

Jean-Pierre R. Falet

Brennan Nichyporuk

Changjian Shui

Douglas Arnold

Sotirios A. Tsaftaris

Tal Arbel

We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain l… (voir plus)esions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI to ensure the clinical validity of generated samples; (2) preserves and disentangles finer pathological details pertaining to a patient's disease state. Additionally, we experiment with an alternative training configuration where we provide supervision to a subset of latent units. It is shown that (1) a partially supervised latent space achieves a higher degree of disentanglement between evidence of disease and subject-specific anatomy; (2) when the prior is formulated with an autoregressive structure, knowledge from the supervision can propagate to the unsupervised latent units, resulting in more informative latent representations capable of modelling anatomy-pathology interdependencies.

2022-11-14

ArXiv (prépublication)

doi.org

arxiv.org

Identification of Novel Cell Surface Therapeutic Targets for KMT2A-Rearranged Acute Myeloid Leukemia

Louis Theret

Marie-Eve Bordeleau

Azadeh Hajmirza

Arnaud Metois

Ossama Moujaber

Éric Audemard

Jean-François Spinella

Jalila Chagraoui

Léo Aubert

Azer Farah

Véronique Lisi

Éric Bonneil

Isabel Boivin

Nadine Mayotte

Tara MacRae

Pierre Thibault

Sébastien Lemieux

Vincent-Philippe Lavallee

Josée Hébert

Guy Sauvageau … (voir 1 de plus)

Philippe P Roux

2022-11-14

Blood (publié)

doi.org

IL1R1 Expression Predicts the Benefit from Allogeneic Hematopoietic Stem Cell Transplantation in Patients with Acute Myeloid Leukemia and Intermediate-Risk Cytogenetics

Guillaume Richard-Carpentier

Francois Beliveau

Sandrine Lacoste

Jean-François Spinella

Michael Vladovsky

Patrick Gendron

Sébastien Lemieux

Vincent-Philippe Lavallee

Guy Sauvageau

Josée Hébert

2022-11-14

Blood (publié)

doi.org

Targeting PLZF Enhances Glucocorticoid Sensitivity in Acute Myeloid Leukemia

Azadeh Hajmirza

Laura Simon

Jalila Chagraoui

Nadine Mayotte

Jean-François Spinella

Tara MacRae

Bernhard Lehnertz

Thierry Bertomeu

Jasmin Coulombe-Huntington

Geneviève Boucher

Sébastien Lemieux

Mike Tyers

Josée Hébert

Guy Sauvageau

2022-11-14

Blood (publié)

doi.org

Teaching Algorithmic Reasoning via In-context Learning

Hattie Zhou

Azade Nova

Hugo Larochelle

Aaron Courville

Behnam Neyshabur

Hanie Sedghi

2022-11-14

ArXiv (prépublication)

doi.org

openreview.net

NeurIPS 2022 Competition: Driving SMARTS

Amir Hossein Rasouli

R. Goebel

Matthew E. Taylor

Iuliia Kotseruba

Soheil Alizadeh

Tianpei Yang

Montgomery Alban

Florian Shkurti

Yuzheng Zhuang

Adam Ścibior

Kasra Rezaee

Animesh Garg

David Meger

Jun Luo

Liam Paull

Weinan Zhang

Xinyu Wang

Xiangshan Chen

2022-11-13

ArXiv (prépublication)

doi.org

arxiv.org

PatchBlender: A Motion Prior for Video Transformers

Gabriele Prato

Yale Song

Janarthanan Rajendran

R Devon Hjelm

Neel Joshi

A. Chandar

2022-11-10

ArXiv (prépublication)

doi.org

openreview.net

Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: A systematic review and meta-analysis of standardized population-based studies

Isabel Bergeri

Mairead Whelan

Harriet Ware

Lorenzo Subissi

Anthony Nardone

Hannah C. Lewis

Zihan Li

Xiaomeng Ma

Marta Valenciano

Brianna Cheng

Lubna Al Ariqi

Arash Rashidian

Joseph Okeibunor

Tasnim Azim

Pushpa Wijesinghe

Linh-Vi Le

Aisling Vaughan

Richard Pebody

Andrea Vicari

Tingting Yan … (voir 9 de plus)

Mercedes Yanes-Lane

Christian Cao

David A. Clifton

Matthew P. Cheng

Jesse Papenburg

David L Buckeridge

Niklas Bobrovitz

Rahul K. Arora

Maria D Van Kerkhove

2022-11-09

PLOS Medicine (publié)

doi.org

Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection

Bonaventure F. P. Dossou

Dianbo Liu

Xu Ji

Moksh Jain

Almer M. van der Sloot

Roger Palou

Michael Tyers

Yoshua Bengio

As antibiotic-resistant bacterial strains are rapidly spreading worldwide, infections caused by these strains are emerging as a global crisi… (voir plus)s causing the death of millions of people every year. Antimicrobial Peptides (AMPs) are one of the candidates to tackle this problem because of their potential diversity, and ability to favorably modulate the host immune response. However, large-scale screening of new AMP candidates is expensive, time-consuming, and now affordable in developing countries, which need the treatments the most. In this work, we propose a novel active machine learning-based framework that statistically minimizes the number of wet-lab experiments needed to design new AMPs, while ensuring a high diversity and novelty of generated AMPs sequences, in multi-rounds of wet-lab AMP screening settings. Combining recurrent neural network models and a graph-based filter (GraphCC), our proposed approach delivers novel and diverse candidates and demonstrates better performances according to our defined metrics.

2022-11-09

Research Square (Research Square) (accepté)

doi.org

arxiv.org

SVRG meets AdaGrad: painless variance reduction

Benjamin Dubois-Taine

Sharan Vaswani

Reza Babanezhad Harikandeh

Mark Schmidt

Simon Lacoste-Julien

2022-11-09

Machine Learning (publié)