5 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…
Error estimates between SGD with momentum and underdamped Langevin diffusion
Arnaud Guillin, Yu Wang, Lihu Xu +1
Stochastic gradient descent with momentum is a popular variant of stochastic gradient descent, which has recently been reported to have a close relationship with the underdamped La…