Publications

Graph embedding and transfer learning can help predict potential species interaction networks despite data limitations
Tanya Strydom
Salomé Bouskila
Francis Banville
Ceres Barros
Dominique Caron
Maxwell J. Farrell
Marie‐Josée Fortin
Benjamin Mercier
Laura J. Pollock
Rogini Runghen
Giulio V. Dalla Riva
Timothée Poisot
Metawebs (networks of potential interactions within a species pool) are a powerful abstraction to understand how large‐scale species inter… (voir plus)action networks are structured. Because metawebs are typically expressed at large spatial and taxonomic scales, assembling them is a tedious and costly process; predictive methods can help circumvent the limitations in data deficiencies, by providing a first approximation of metawebs. One way to improve our ability to predict metawebs is to maximize available information by using graph embeddings, as opposed to an exhaustive list of species interactions. Graph embedding is an emerging field in machine learning that holds great potential for ecological problems. Here, we outline how the challenges associated with inferring metawebs line‐up with the advantages of graph embeddings; followed by a discussion as to how the choice of the species pool has consequences on the reconstructed network, specifically as to the role of human‐made (or arbitrarily assigned) boundaries and how these may influence ecological hypotheses.
2023-10-18
Methods in Ecology and Evolution (publié)
doi.org
Mean-field games among teams
Jayakumar Subramanian
Akshat Kumar
Aditya Mahajan
2023-10-17
ArXiv (prépublication)
doi.org
arxiv.org
Studying the characteristics of AIOps projects on GitHub
Roozbeh Aghili
Heng Li
Foutse Khomh
2023-10-17
Empirical Software Engineering (publié)
doi.org
arxiv.org
Interpreting and Controlling Vision Foundation Models via Text Explanations
Haozhe Chen
Junfeng Yang
Carl Vondrick
Chengzhi Mao
2023-10-15
ArXiv (prépublication)
doi.org
arxiv.org
Transparent Anomaly Detection via Concept-based Explanations
Laya Rafiee Sevyeri
Ivaxi Sheth
Farhood Farahnak
S. Enger
2023-10-15
ArXiv (prépublication)
doi.org
arxiv.org
ConText-GAN: using contextual texture information for realistic and controllable medical image synthesis*
Marc Adrien Hostin
Shahram Attarian
David Bendahan
Bellemare Marc-Emmanuel
This study proposes an enhancement to the ConText-GAN, an image synthesis model using a controllable texture input. The improvement consists… (voir plus) in using a texture feature fusion module to reduce the complexity of the model, and enable the use of the OASIS architecture for image generation.
2023-10-14
2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (publié)
doi.org
RelationalUNet for Image Segmentation
Ivaxi Sheth
Pedro H. M. Braga
Shiva Kanth Sujit
Sahar Dastani
S Ebrahimi Kahou
2023-10-14
Machine Learning in Medical Imaging (publié)
doi.org
Defining Feasibility as a Criterion for Essential Surgery: A Qualitative Study with Global Children’s Surgery Experts
Alizeh Abbas
Henry E. Rice
Dan Poenaru
Lubna Samad
2023-10-13
World Journal of Surgery (publié)
doi.org
Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN
Florence Regol
Joud Chataoui
Mark J. Coates
2023-10-12
ArXiv (prépublication)
doi.org
arxiv.org
Predicting Solar PV Output Based on Hybrid Deep Learning and Physical
Models: Case Study of Morocco
Samira Abousaid
Loubna Benabbou
Hanane Dagdougui
Ismail Belhaj
Abdelaziz Berrado
Hicham Bouzekri
2023-10-12
Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) (publié)
doi.org
Prognosis of critically ill immunocompromised patients with virus-detected acute respiratory failure
Guillaume Dumas
Maxime Bertrand
Virginie Lemiale
Emmanuel Canet
François Barbier
Achille Kouatchet
Alexandre Demoule
Kada Klouche
Anne-Sophie Moreau
Laurent Argaud
Florent Wallet
Jean Herlé Raphalen
Djamel Mokart
Fabrice Bruneel
Frédéric Pène
Elie Azoulay
2023-10-12
Annals of Intensive Care (publié)
doi.org
Summary of the Fourth International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2023)
Matteo Biagiola
Nicolás Cardozo
Donghwan Shin
Foutse Khomh
Andrea Stocco
Vincenzo Riccio
2023-10-12
ACM SIGSOFT Softw. Eng. Notes (publié)
doi.org