Publications

Bio-Mechanical Poet: An Immersive Audiovisual Playground for Brain Signals and Generative AI.
Philipp Thölke
Antoine Bellemare‐Pepin
Yann Harel
François Lespinasse
Karim Jerbi CoCo Lab
2023-12-31
ICCC (publié)
dblp.uni-trier.de
Building on Efficient Foundations: Effective Training of LLMs with Structured Feedforward Layers.
Xiuying Wei
Skander Moalla
Razvan Pascanu
Caglar Gulçehre
2023-12-31
Advances in Neural Information Processing Systems 37 (publié)
doi.org
CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization
Wenzheng Hu
Ning Liu
Zhengping Che
Mingyang Li
Jian Tang
Changshui Zhang
Jianqiang Wang
Deep convolutional neural networks are shown to be overkill with high parametric and computational redundancy in many application scenarios,… (voir plus) and an increasing number of works have explored model pruning to obtain lightweight and efficient networks. However, most existing pruning approaches are driven by empirical heuristics and rarely consider the joint impact of channels, leading to unguaranteed and suboptimal performance. In this article, we propose a novel channel pruning method via class-aware trace ratio optimization (CATRO) to reduce the computational burden and accelerate the model inference. Utilizing class information from a few samples, CATRO measures the joint impact of multiple channels by feature space discriminations and consolidates the layerwise impact of preserved channels. By formulating channel pruning as a submodular set function maximization problem, CATRO solves it efficiently via a two-stage greedy iterative optimization procedure. More importantly, we present theoretical justifications on convergence of CATRO and performance of pruned networks. Experimental results demonstrate that CATRO achieves higher accuracy with similar computation cost or lower computation cost with similar accuracy than other state-of-the-art channel pruning algorithms. In addition, because of its class-aware property, CATRO is suitable to prune efficient networks adaptively for various classification subtasks, enhancing handy deployment and usage of deep networks in real-world applications.
2023-12-31
IEEE Trans. Neural Networks Learn. Syst. (publié)
doi.org
arxiv.org
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces
Ahmad-reza Ehyaei
Kiarash Mohammadi
Amir-Hossein Karimi
Samira Samadi
Golnoosh Farnadi
As responsible AI gains importance in machine learning algorithms, properties such as fairness, adversarial robustness, and causality have r… (voir plus)eceived considerable attention in recent years. However, despite their individual significance, there remains a critical gap in simultaneously exploring and integrating these properties. In this paper, we propose a novel approach that examines the relationship between individual fairness, adversarial robustness, and structural causal models in heterogeneous data spaces, particularly when dealing with discrete sensitive attributes. We use causal structural models and sensitive attributes to create a fair metric and apply it to measure semantic similarity among individuals. By introducing a novel causal adversarial perturbation and applying adversarial training, we create a new regularizer that combines individual fairness, causality, and robustness in the classifier. Our method is evaluated on both real-world and synthetic datasets, demonstrating its effectiveness in achieving an accurate classifier that simultaneously exhibits fairness, adversarial robustness, and causal awareness.
2023-12-31
AAAI (publié)
doi.org
arxiv.org
Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations
Connor Stone
Alexandre Adam
Adam Coogan
M. J. Yantovski-Barth
Andreas Filipp
Landung Setiawan
Cordero Core
Ronan Legin
Charles Wilson
Gabriel Missael Barco
Yashar Hezaveh
Laurence Perreault-Levasseur
2023-12-31
Journal of Open Source Software (publié)
doi.org
arxiv.org
ChainBuddy: An AI-assisted Agent System for Helping Users Set up LLM Pipelines
jingyue zhang
Ian Arawjo
2023-12-31
UIST (Adjunct Volume) (publié)
doi.org
Challenges in multi-task learning for fMRI-based diagnosis: Benefits for psychiatric conditions and CNVs would likely require thousands of patients
Annabelle Harvey
Clara A. Moreau
Kuldeep Kumar
Guillaume Huguet
Sebastian G.W. Urchs
Hanad Sharmarke
Khadije Jizi
Charles-Olivier Martin
Nadine Younis
Petra Tamer
Jean-Louis Martineau
Pierre Orban
Ana Isabel Silva
Jeremy Hall
Marianne B.M. van den Bree
Michael J. Owen
David E.J. Linden
Sarah Lippé
Carrie E. Bearden
Guillaume Dumas … (voir 2 de plus)
Sébastien Jacquemont
Pierre Bellec
There is a growing interest in using machine learning (ML) models to perform automatic diagnosis of psychiatric conditions; however, general… (voir plus)ising the prediction of ML models to completely independent data can lead to sharp decrease in performance. Patients with different psychiatric diagnoses have traditionally been studied independently, yet there is a growing recognition of neuroimaging signatures shared across them as well as rare genetic copy number variants (CNVs). In this work, we assess the potential of multi-task learning (MTL) to improve accuracy by characterising multiple related conditions with a single model, making use of information shared across diagnostic categories and exposing the model to a larger and more diverse dataset. As a proof of concept, we first established the efficacy of MTL in a context where there is clearly information shared across tasks: the same target (age or sex) is predicted at different sites of data collection in a large functional magnetic resonance imaging (fMRI) dataset compiled from multiple studies. MTL generally led to substantial gains relative to independent prediction at each site. Performing scaling experiments on the UK Biobank, we observed that performance was highly dependent on sample size: for large sample sizes (N > 6000) sex prediction was better using MTL across three sites (N = K per site) than prediction at a single site (N = 3K), but for small samples (N 500) MTL was actually detrimental for age prediction. We then used established machine-learning methods to benchmark the diagnostic accuracy of each of the 7 CNVs (N = 19–103) and 4 psychiatric conditions (N = 44–472) independently, replicating the accuracy previously reported in the literature on psychiatric conditions. We observed that MTL hurt performance when applied across the full set of diagnoses, and complementary analyses failed to identify pairs of conditions which would benefit from MTL. Taken together, our results show that if a successful multi-task diagnostic model of psychiatric conditions were to be developed with resting-state fMRI, it would likely require datasets with thousands of patients across different diagnoses.
2023-12-31
Imaging Neuroscience (publié)
doi.org
ChatGPT: What Every Pediatric Surgeon Should Know About Its Potential Uses and Pitfalls
Raquel González
Dan Poenaru
Russell Woo
A Francois Trappey
Stewart Carter
David Darcy
Ellen Encisco
Brian Gulack
Doug Miniati
Edzhem Tombash
Eunice Y. Huang
2023-12-31
Journal of Pediatric Surgery (publié)
doi.org
CL-MASR: A Continual Learning Benchmark for Multilingual ASR
Luca Della Libera
Pooneh Mousavi
Salah Zaiem
Yusuf Cem Sübakan
Mirco Ravanaelli
2023-12-31
IEEE ACM Trans. Audio Speech Lang. Process. (publié)
doi.org
arxiv.org
Combine and Conquer: A Meta-Analysis on Data Shift and Out-of-Distribution Detection
Eduardo Dadalto Câmara Gomes
Florence Alberge
Pierre Duhamel
Pablo Piantanida
2023-12-31
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
Common Challenges of Deep Reinforcement Learning Applications Development: An Empirical Study
Mohammad Mehdi Morovati
Florian Tambon
Mina Taraghi
Amin Nikanjam
Foutse Khomh
Machine Learning (ML) is increasingly being adopted in different industries. Deep Reinforcement Learning (DRL) is a subdomain of ML used to … (voir plus)produce intelligent agents. Despite recent developments in DRL technology, the main challenges that developers face in the development of DRL applications are still unknown. To fill this gap, in this paper, we conduct a large-scale empirical study of 927 DRL-related posts extracted from Stack Overflow, the most popular Q&A platform in the software community. Through the process of labeling and categorizing extracted posts, we created a taxonomy of common challenges encountered in the development of DRL applications, along with their corresponding popularity levels. This taxonomy has been validated through a survey involving 65 DRL developers. Results show that at least 45% of developers experienced 18 of the 21 challenges identified in the taxonomy. The most frequent source of difficulty during the development of DRL applications are Comprehension, API usage, and Design problems, while Parallel processing, and DRL libraries/frameworks are classified as the most difficult challenges to address, with respect to the time required to receive an accepted answer. We hope that the research community will leverage this taxonomy to develop efficient strategies to address the identified challenges and improve the quality of DRL applications.
2023-12-31
Empir. Softw. Eng. (publié)
doi.org
arxiv.org
Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning
Tianyu Li
Doina Precup
Guillaume Rabusseau
In this paper, we present connections between three models used in different research fields: weighted finite automata~(WFA) from formal lan… (voir plus)guages and linguistics, recurrent neural networks used in machine learning, and tensor networks which encompasses a set of optimization techniques for high-order tensors used in quantum physics and numerical analysis. We first present an intrinsic relation between WFA and the tensor train decomposition, a particular form of tensor network. This relation allows us to exhibit a novel low rank structure of the Hankel matrix of a function computed by a WFA and to design an efficient spectral learning algorithm leveraging this structure to scale the algorithm up to very large Hankel matrices.We then unravel a fundamental connection between WFA and second-orderrecurrent neural networks~(2-RNN): in the case of sequences of discrete symbols, WFA and 2-RNN with linear activationfunctions are expressively equivalent. Leveraging this equivalence result combined with the classical spectral learning algorithm for weighted automata, we introduce the first provable learning algorithm for linear 2-RNN defined over sequences of continuous input vectors.This algorithm relies on estimating low rank sub-blocks of the Hankel tensor, from which the parameters of a linear 2-RNN can be provably recovered. The performances of the proposed learning algorithm are assessed in a simulation study on both synthetic and real-world data.
2023-12-31
Mach. Learn. (publié)
doi.org
arxiv.org