Publications

AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery

Amirhossein Abaskohi

Amrutha Varshini Ramesh

Shailesh Nanisetty

Chirag Goel

David Vazquez

Chris Pal

Spandana Gella

Giuseppe Carenini

Issam Hadj Laradji

2025-04-10

ArXiv (prépublication)

arxiv.org

Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm

Amir Ali Farzin

Yuen-Man Pun

Philipp Braun

Antoine Lesage-Landry

Youssef Diouane

Iman Shames

2025-04-10

ArXiv (prépublication)

arxiv.org

Burst firing optimizes invariant coding of natural communication signals by electrosensory neural populations

Michael G. Metzen

Amin Akhshi

Pouya Bashivan

Anmar Khadra

Maurice J. Chacron

2025-04-09

iScience (publié)

doi.org

Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things

Saeid Jamshidi

Amin Nikanjam

Nafi Kawser Wazed

Foutse Khomh

2025-04-09

ArXiv (prépublication)

arxiv.org

Lugha-Llama: Adapting Large Language Models for African Languages

Happy Buzaaba

Alexander Wettig

David Ifeoluwa Adelani

Christiane Fellbaum

2025-04-09

ArXiv (prépublication)

arxiv.org

Alignment of auditory artificial networks with massive individual fMRI brain data leads to generalisable improvements in brain encoding and downstream tasks

Maelle Freteault

Maximilien Le Clei

Loic Tetrel

Lune Bellec

Nicolas Farrugia

Artificial neural networks trained in the field of artificial intelligence (AI) have emerged as key tools to model brain processes, sparking… (voir plus) the idea of aligning network representations with brain dynamics to enhance performance on AI tasks. While this concept has gained support in the visual domain, we investigate here the feasibility of creating auditory artificial neural models directly aligned with individual brain activity. This objective raises major computational challenges, as models have to be trained directly with brain data, which is typically collected at a much smaller scale than data used to train AI models. We aimed to answer two key questions: (1) Can brain alignment of auditory models lead to improved brain encoding for novel, previously unseen stimuli? (2) Can brain alignment lead to generalisable representations of auditory signals that are useful for solving a variety of complex auditory tasks? To answer these questions, we relied on two massive datasets: a deep phenotyping dataset from the Courtois neuronal modelling project, where six subjects watched four seasons (36 hours) of the Friends TV series in functional magnetic resonance imaging and the HEAR benchmark, a large battery of downstream auditory tasks. We fine-tuned SoundNet, a small pretrained convolutional neural network with ∼2.5M parameters. Aligning SoundNet with brain data from three seasons of Friends led to substantial improvement in brain encoding in the fourth season, extending beyond auditory and visual cortices. We also observed consistent performance gains on the HEAR benchmark, particularly for tasks with limited training data, where brain-aligned models performed comparably to the best-performing models regardless of size. We finally compared individual and group models, finding that individual models often matched or outperformed group models in both brain encoding and downstream task performance, highlighting the data efficiency of fine-tuning with individual brain data. Our results demonstrate the feasibility of aligning artificial neural network representations with individual brain activity during auditory processing, and suggest that this alignment is particularly beneficial for tasks with limited training data. Future research is needed to establish whether larger models can achieve even better performance and whether the observed gains extend to other tasks, particularly in the context of few shot learning.

2025-04-08

Imaging Neuroscience (publié)

doi.org

Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors

Connor Bottrell

Laurence Perreault-Levasseur

Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant… (voir plus) barriers to such analysis are the non-trivial noise properties of real astronomical images and the point spread function (PSF) which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed \emph{Hubble Space Telescope} (\emph{HST}) data, recovers structures which have otherwise only become visible in next-generation \emph{James Webb Space Telescope} (\emph{JWST}) imaging.

2025-04-08

The Astronomical Journal (publié)

doi.org

arxiv.org

InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured Data

David R. Johnson

Smita Krishnaswamy

Michael Perlmutter

2025-04-08

ArXiv (prépublication)

arxiv.org