Publications

Predicting greenhouse gas Emissions in Shipping: A Case Study Of Canada

Abdelhak EL AISSI

Loubna Benabbou

Abdelaziz Berrado

Stephane Carron

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

doi.org

Prism: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search

Vahid Majdinasab

Amin Nikanjam

Foutse Khomh

2025-04-07

ArXiv (prépublication)

arxiv.org

View-Dependent Deformation Fields for 2D Editing of 3D Models

Martin El Mqirmi

Noam Aigerman

2025-04-07

ArXiv (prépublication)

arxiv.org

Graph Neural Networks Meet Probabilistic Graphical Models: A Survey

Chenqing Hua

Sitao Luan

Qian Zhang

Jie Fu

Guy Wolf

2025-04-06

IEEE International Conference on Acoustics, Speech, and Signal Processing (publié)

doi.org

Latent Representation Learning for Multimodal Brain Activity Translation

Arman Afrasiyabi

Dhananjay Bhaskar

Erica Lindsey Busch

Laurent Caplette

Rahul Singh

Guillaume Lajoie

Nicholas B Turk-Browne

Smita Krishnaswamy

Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordi… (voir plus)ngs such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts.

2025-04-06

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

doi.org

arxiv.org

Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognition

Muhammad Osama Zeeshan

Marco Pedersoli

Alessandro Lameiras Koerich

Eric Grange

2025-04-05

ArXiv (prépublication)

arxiv.org