24 citations · 37 across the 8 of their papers we have counts for
9 papers
RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
Nicholas H. Barbara, Max Revay, Ruigang Wang +2
Neural networks are typically sensitive to small input perturbations, leading to unexpected or brittle behaviour. We present RobustNeuralNetworks.jl: a Julia package for neural net…
Learning over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System
Ruigang Wang, Nicholas H. Barbara, Max Revay +1
This paper proposes a nonlinear policy architecture for control of partially-observed linear dynamical systems providing built-in closed-loop stability guarantees. The policy is ba…
Contraction-Based Methods for Stable Identification and Robust Machine Learning: a Tutorial
Ian R. Manchester, Max Revay, Ruigang Wang
This tutorial paper provides an introduction to recently developed tools for machine learning, especially learning dynamical systems (system identification), with stability and rob…
Distributed Identification of Contracting and/or Monotone Network Dynamics
Max Revay, Jack Umenberger, Ian R. Manchester
This paper proposes methods for identification of large-scale networked systems with guarantees that the resulting model will be contracting -- a strong form of nonlinear stability…
Recurrent Equilibrium Networks: Flexible Dynamic Models with Guaranteed Stability and Robustness
Max Revay, Ruigang Wang, Ian R. Manchester
This paper introduces recurrent equilibrium networks (RENs), a new class of nonlinear dynamical models} for applications in machine learning, system identification and control. The…
Lipschitz Bounded Equilibrium Networks
Max Revay, Ruigang Wang, Ian R. Manchester
This paper introduces new parameterizations of equilibrium neural networks, i.e. networks defined by implicit equations. This model class includes standard multilayer and residual…