4 papers
Quantifying Uncertainty In Wide Two-Layer Neural Networks: On The Law Of The Limiting Fluctuation Process
Arnaud Descours, Arnaud Guillin, Geoffrey Lacour +3
Uncertainty quantification in neural networks prediction is a main issue for usual applications. Our approach seeks at reducing computation costs by directly evaluating uncertainty…
Activity-driven clustering and many-body steady state of jamming run-and-tumble particles
Leo Hahn, Arnaud Guillin, Manon Michel
We exactly resolve the three-particle steady state of run-and-tumble particles with jamming interactions, providing the first microscopic description beyond two bodies. The invaria…
Convergence of non-reversible Markov processes via lifting and flow Poincar{é} inequality
Andreas Eberle, Arnaud Guillin, Leo Hahn +2
We propose a general approach for quantitative convergence analysis of non-reversible Markov processes, based on the concept of second-order lifts and a variational approach to hyp…
Long-time analysis of a pair of on-lattice and continuous run-and-tumble particles with jamming interactions
Arnaud Guillin, Leo Hahn, Manon Michel
Run-and-Tumble Particles (RTPs) are a key model of active matter. They are characterized by alternating phases of linear travel and random direction reshuffling. By this dynamic be…