15 citations · 17 across the 4 of their papers we have counts for
4 papers
Distributional simplicity bias and effective convexity in Energy Based Models
Aurélien Decelle, Alfonso de Jesús Navas Gómez, Beatriz Seoane
Energy-based learning is a powerful framework for generative modelling, but its training is inherently non-convex, leading potentially to sensitivity to initialisation, poor local…
Inferring effective interactions and task-related brain states from large-scale neural activity with Restricted Boltzmann Machines
Nicolas Béreux, Giovanni Catania, Aurélien Decelle +3
Large-scale electrophysiological recordings now enable the simultaneous monitoring of thousands of neurons across multiple brain regions, revealing structured variability in popula…
Inferring Higher-Order Couplings with Neural Networks
Aurélien Decelle, Alfonso de Jesús Navas Gómez, Beatriz Seoane
Maximum entropy methods, rooted in the inverse Ising/Potts problem from statistical physics, are widely used to model pairwise interactions in complex systems across disciplines su…
Inferring effective couplings with Restricted Boltzmann Machines
Aurélien Decelle, Cyril Furtlehner, Alfonso De Jesus Navas Gómez +1
Generative models offer a direct way of modeling complex data. Energy-based models attempt to encode the statistical correlations observed in the data at the level of the Boltzmann…