Publications

Behavioural equivalences for continuous-time Markov processes
Linan Chen
Florence Clerc
Prakash Panangaden
2023-03-30
Mathematical Structures in Computer Science (published)
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 (published)
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 … (see 7 more)
Christian Beckmann
Antonio Persico
Tobias Banaschewski
Sarah Durston
Christine M. Freitag
Declan Murphy
Christine Ecker
2023-03-29
Molecular Psychiatry (published)
doi.org
FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation
Zhuoran Xiong
Marihan Amein
O. Therrien
Warren Gross
Brett Meyer
2023-03-29
ArXiv (preprint)
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 (accepted)
openreview.net
openreview.net
Multi-view manifold learning of human brain state trajectories
Erica Lindsey Busch
Je-chun Huang
Andrew Benz
Tom Wallenstein
Guillaume Lajoie
Guy Wolf
Smita Krishnaswamy
Nicholas Turk-Browne
2023-03-27
Nature Computational Science (published)
doi.org
Green Federated Learning
Ashkan Yousefpour
Sheng Guo
Ashish V. Shenoy
Sayan Ghosh
Pierre Stock
Kiwan Maeng
Schalk-Willem Kruger
Michael Rabbat
Carole-Jean Wu
Ilya Mironov
The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets. As a consequenc… (see more)e, the amount of compute used in training state-of-the-art models is exponentially increasing (doubling every 10 months between 2015 and 2022), resulting in a large carbon footprint. Federated Learning (FL) - a collaborative machine learning technique for training a centralized model using data of decentralized entities - can also be resource-intensive and have a significant carbon footprint, particularly when deployed at scale. Unlike centralized AI that can reliably tap into renewables at strategically placed data centers, cross-device FL may leverage as many as hundreds of millions of globally distributed end-user devices with diverse energy sources. Green AI is a novel and important research area where carbon footprint is regarded as an evaluation criterion for AI, alongside accuracy, convergence speed, and other metrics. In this paper, we propose the concept of Green FL, which involves optimizing FL parameters and making design choices to minimize carbon emissions consistent with competitive performance and training time. The contributions of this work are two-fold. First, we adopt a data-driven approach to quantify the carbon emissions of FL by directly measuring real-world at-scale FL tasks running on millions of phones. Second, we present challenges, guidelines, and lessons learned from studying the trade-off between energy efficiency, performance, and time-to-train in a production FL system. Our findings offer valuable insights into how FL can reduce its carbon footprint, and they provide a foundation for future research in the area of Green AI.
2023-03-26
ArXiv (preprint)
doi.org
arxiv.org
Catalyzing next-generation Artificial Intelligence through NeuroAI
Anthony Zador
Sean Escola
Blake Richards
Bence Ölveczky
Yoshua Bengio
Kwabena Boahen
Matthew Botvinick
Dmitri Chklovskii
Anne Churchland
Claudia Clopath
James DiCarlo
Surya
Surya Ganguli
Jeff Hawkins
Konrad Paul Kording
Alexei Koulakov
Yann LeCun
Timothy P. Lillicrap
Adam
Adam Marblestone … (see 9 more)
Bruno Olshausen
Alexandre Pouget
Cristina Savin
Terrence Sejnowski
Eero Simoncelli
Sara Solla
David Sussillo
Andreas S. Tolias
Doris Tsao
2023-03-22
Nature Communications (published)
doi.org
A Novel Model for Novelty: Modeling the Emergence of Innovation from Cumulative Culture
Natalie Kastel
Guillaume Dumas
2023-03-22
Active Inference (published)
doi.org
Posthoc Interpretation via Quantization
Cem (Yusuf) Subakan
Francesco Paissan
Mirco Ravanelli
In this paper, we introduce a new approach, called Posthoc Interpretation via Quantization (PIQ), for interpreting decisions made by trained… (see more) classifiers. Our method utilizes vector quantization to transform the representations of a classifier into a discrete, class-specific latent space. The class-specific codebooks act as a bottleneck that forces the interpreter to focus on the parts of the input data deemed relevant by the classifier for making a prediction. Our model formulation also enables learning concepts by incorporating the supervision of pretrained annotation models such as state-of-the-art image segmentation models. We evaluated our method through quantitative and qualitative studies involving black-and-white images, color images, and audio. As a result of these studies we found that PIQ generates interpretations that are more easily understood by participants to our user studies when compared to several other interpretation methods in the literature.
2023-03-22
ArXiv (preprint)
doi.org
arxiv.org
Electromagnetic interference shielding in lightweight carbon xerogels
Biporjoy Sarkar
Floriane Miquet-Westphal
Sanyasi Bobbara
Ben George
Giovanni Beltrame
David Dousset
Ke Wu
Fabio Cicoira
With the increasing use of high-frequency electronic and wireless devices, electromagnetic interference (EMI) has become a growing concern d… (see more)ue to its potential impact on both electronic devices and human health. In this study, we demonstrated the performance of lightweight, electrically conducting 3D resorcinol-formaldehyde carbon xerogels, of 2.4 mm thickness, as an EMI shieldin the frequency range of 10–15 GHz (X-Ku band). The brittle carbon xerogels revealed complex porous structures with irregularly shaped pores that were randomly distributed. Electrochemical characterization revealed that the material behaved as an electrical double-layer capacitor. The carbon xerogels displayed reflection-dominated (∼ 84%) shielding behavior, with a total EMI shielding effectiveness (SE) value of ∼ 61 dB. The absorption process also contributed (∼ 16%) to the total SE. This behavior is attributed to the carbon xerogels' complex porous network, which effectively suppresses EM waves.
2023-03-21
Materials Research Express (published)
doi.org
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT
Vincent Andrearczyk
Valentin Oreiller
Moamen A. Abobakr
Azadeh Akhavanallaf
Panagiotis Balermpas
Sarah Boughdad
Leo Capriotti
Joel Castelli
Catherine Cheze Le Rest
Pierre Decazes
Ricardo Correia
D. El-Habashy
Hesham M. Elhalawani
C. Fuller
Mario Jreige
Yomna Khamis
Agustina La Greca Saint-Esteven
A. Mohamed
M. Naser
John O. Prior … (see 11 more)
Su Ruan
Stephanie Tanadini-Lang
Olena Tankyevych
Yazdan Salimi
Martin Vallières
Pierre Véra
Dimitris Visvikis
K. Wahid
Habib Zaidi
Mathieu Hatt
Adrien Depeursinge
2023-03-18
Lecture Notes in Computer Science (published)
doi.org