12 citations · 13 across the 8 of their papers we have counts for
5 papers · 1 filter
Safe-by-Design Learning via Energy-based Neural Networks
Simone Betteti, Morteza Lahijanian, Luca Laurenti
Learning neural-network models of dynamical systems with safety guarantees is a fundamental requirement for their deployment in safety-critical settings. Safety is commonly establi…
Timescale Limits of Linear-Threshold Networks
William Retnaraj, Simone Betteti, Alexander Davydov +2
Linear-threshold networks (LTNs) capture the mesoscale behavior of interacting populations of neurons and are of particular interest to control theorists due to their dynamical ric…
Hybrid Energy-Based Models for Physical AI: Provably Stable Identification of Port-Hamiltonian Dynamics
Simone Betteti, Luca Laurenti
Energy-based models (EBMs) implement inference as gradient descent on a learned Lyapunov function, yielding interpretable, structure-preserving alternatives to black-box neural ODE…
A Dynamical Theory of Sequential Retrieval in Input-Driven Hopfield Networks
Simone Betteti, Giacomo Baggio, Sandro Zampieri
Reasoning is the ability to integrate internal states and external inputs in a meaningful and semantically consistent flow. Contemporary machine learning (ML) systems increasingly…
Incremental Input-to-State Stability and Equilibrium Tracking for Stochastic Contracting Dynamics
Yu Kawano, Simone Betteti, Alexander Davydov +1
In this paper, we study the contractivity of nonlinear stochastic differential equations (SDEs) driven by deterministic inputs and Brownian motions. Given a weighted -norm…