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Validation Diagnostics for SBI algorithms based on Normalizing Flows
Julia Linhart, Alexandre Gramfort, Pedro L. C. Rodrigues
Building on the recent trend of new deep generative models known as Normalizing Flows (NF), simulation-based inference (SBI) algorithms can now efficiently accommodate arbitrary co…
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels
Guillaume Staerman, Cédric Allain, Alexandre Gramfort +1
Temporal point processes (TPP) are a natural tool for modeling event-based data. Among all TPP models, Hawkes processes have proven to be the most widely used, mainly due to their…
Data augmentation for learning predictive models on EEG: a systematic comparison
Cédric Rommel, Joseph Paillard, Thomas Moreau +1
Objective: The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the re…
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Thomas Moreau, Mathurin Massias, Alexandre Gramfort +18
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.…
Toward a realistic model of speech processing in the brain with self-supervised learning
Juliette Millet, Charlotte Caucheteux, Pierre Orhan +5
Several deep neural networks have recently been shown to generate activations similar to those of the brain in response to the same input. These algorithms, however, remain largely…
Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments
Hicham Janati, Marco Cuturi, Alexandre Gramfort
Several fields in science, from genomics to neuroimaging, require monitoring populations (measures) that evolve with time. These complex datasets, describing dynamics with both tim…