Publications

Machine-learning-based arc selection for constrained shortest path problems in column generation
Mouad Morabit
Guy Desaulniers
Andrea Lodi
Column generation is an iterative method used to solve a variety of optimization problems. It decomposes the problem into two parts: a maste… (voir plus)r problem and one or more pricing problems (PP). The total computing time taken by the method is divided between these two parts. In routing or scheduling applications, the problems are mostly defined on a network, and the PP is usually an NP-hard shortest path problem with resource constraints. In this work, we propose a new heuristic pricing algorithm based on machine learning. By taking advantage of the data collected during previous executions, the objective is to reduce the size of the network and accelerate the PP, keeping only the arcs that have a high chance to be part of the linear relaxation solution. The method has been applied to two specific problems: the vehicle and crew scheduling problem in public transit and the vehicle routing problem with time windows. Reductions in computational time of up to 40% can be obtained.
2023-04-01
INFORMS Journal on Optimization (publié)
doi.org
arxiv.org
Picture Cards Versus Physical Examination: A Proof-of-Concept Study to Improve the SOSAS Survey Tool.
Adesoji Ademuyiwa
Benedict C. Nwomeh
Dan Poenaru
Justina O. Seyi-Olajide
Iyabo Y. Ademuyiwa
Tinuola O. Odugbemi
Ogechi Abazie
Oluwaseun A. Ladipo-Ajayi
Olufemi Bankole
Olumide A. Elebute
Babasola Okusanya
Felix M. Alakaloko
Eyitayo O. Alabi
Ayomide Makanjuola
Shailvi Gupta
Tu Tran
Amanda Onwuka A
Emily R. Smith
Riinu Pius
Ewen Harrison … (voir 1 de plus)
Christopher O. Bode
2023-04-01
Journal of Surgical Research (publié)
doi.org
A Survey of Self-Supervised and Few-Shot Object Detection
Gabriel Huang
Issam Hadj Laradji
David Vazquez
Simon Lacoste-Julien
Pau Rodriguez
Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require … (voir plus)dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page: https://gabrielhuang.github.io/fsod-survey/.
2023-04-01
IEEE Transactions on Pattern Analysis and Machine Intelligence (publié)
doi.org
arxiv.org
Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests
Christopher Beckham
Martin Weiss
Florian Golemo
Sina Honari
Derek Nowrouzezahrai
Chris Pal
2023-04-01
Pattern Recognition (publié)
doi.org
openreview.net
Bayesian stroke modeling details sex biases in the white matter substrates of aphasia
Julius M Kernbach
Gesa Hartwigsen
Jae‐Sung Lim
Hee-Joon Bae
Kyung‐Ho Yu
Gottfried Schlaug
Anna K. Bonkhoff
Natalia S. Rost
Danilo Bzdok
2023-03-31
Communications Biology (publié)
doi.org
MLGCN: An Ultra Efficient Graph Convolution Neural Model For 3D Point Cloud Analysis
Mohammad Khodadad
Morteza Rezanejad
Ali Shiraee Kasmaee
Kaleem Siddiqi
Dirk Bernhardt-Walther
Hamidreza Mahyar
2023-03-31
ArXiv (prépublication)
doi.org
arxiv.org
Varepsilon kú mask: Integrating Yorùbá cultural greetings into machine translation
Idris Akinade
Jesujoba Oluwadara Alabi
David Ifeoluwa Adelani
Clement Odoje
Dietrich Klakow
This paper investigates the performance of massively multilingual neural machine translation (NMT) systems in translating Yorùbá greetings… (voir plus) (kú mask), which are a big part of Yorùbá language and culture, into English. To evaluate these models, we present IkiniYorùbá, a Yorùbá-English translation dataset containing some Yorùbá greetings, and sample use cases. We analysed the performance of different multilingual NMT systems including Google and NLLB and show that these models struggle to accurately translate Yorùbá greetings into English. In addition, we trained a Yorùbá-English model by fine-tuning an existing NMT model on the training split of IkiniYorùbá and this achieved better performance when compared to the pre-trained multilingual NMT models, although they were trained on a large volume of data.
2023-03-31
ArXiv (prépublication)
doi.org
arxiv.org
Behavioural equivalences for continuous-time Markov processes
Linan Chen
Florence Clerc
Prakash Panangaden
2023-03-30
Mathematical Structures in Computer Science (publié)
doi.org
Machine learning application development: practitioners’ insights
Md Saidur Rahman
Foutse Khomh
Alaleh Hamidi
Jinghui Cheng
Giuliano Antoniol
Hironori Washizaki
2023-03-30
Software Quality Journal (publié)
doi.org
arxiv.org
Cross-sectional and longitudinal neuroanatomical profiles of distinct clinical (adaptive) outcomes in autism
Charlotte M. Pretzsch
Dorothea L. Floris
Tim Schäfer
Anke Bletsch
Caroline Gurr
Michael V. Lombardo
Chris H. Chatham
Julian Tillmann
Tony Charman
Martina Arenella
Emily J. H. Jones
Sara Ambrosino
Thomas Bourgeron
Guillaume Dumas
Freddy Cliquet
Claire Leblond
Eva Loth
Beth Oakley
Jan K. Buitelaar
Simon Baron-Cohen … (voir 7 de plus)
Christian Beckmann
Antonio Persico
Tobias Banaschewski
Sarah Durston
Christine M. Freitag
Declan Murphy
Christine Ecker
2023-03-29
Molecular Psychiatry (publié)
doi.org
FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation
Zhuoran Xiong
Marihan Amein
Olivier Therrien
Warren Gross
Brett Meyer
2023-03-29
ArXiv (prépublication)
doi.org
arxiv.org
A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection
Marine Picot
Federica Granese
Guillaume Staerman
Marco Romanelli
Francisco Messina
Pablo Piantanida
Pierre Colombo
2023-03-28
TMLR (accepté)
openreview.net
openreview.net