Publications

APP: Anytime Progressive Pruning
Diganta Misra
Bharat Runwal
Tianlong Chen
Zhangyang Wang
Irina Rish
With the latest advances in deep learning, several methods have been investigated for optimal learning settings in scenarios where the data … (see more)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 (accepted)
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 … (see 99 more)
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 (published)
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… (see more)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 (preprint)
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 … (see 1 more)
Philippe P Roux
2022-11-14
Blood (published)
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 (published)
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 (published)
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 (preprint)
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 (preprint)
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 (preprint)
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 … (see 9 more)
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 (published)
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… (see more)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) (accepted)
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 (published)
doi.org
arxiv.org