24 citations · 28 across the 2 of their papers we have counts for
3 papers
cs.LG2020★ 24 cited
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…
cs.LG2020
A Convex Parameterization of Robust Recurrent Neural Networks
Max Revay, Ruigang Wang, Ian R. Manchester
Recurrent neural networks (RNNs) are a class of nonlinear dynamical systems often used to model sequence-to-sequence maps. RNNs have excellent expressive power but lack the stabili…
cs.LG2019★ 4 cited
Contracting Implicit Recurrent Neural Networks: Stable Models with Improved Trainability
Max Revay, Ian R. Manchester
Stability of recurrent models is closely linked with trainability, generalizability and in some applications, safety. Methods that train stable recurrent neural networks, however,…