52 citations · 111 across the 30 of their papers we have counts for
5 papers · 1 filter
Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees
Sean Jaffe, Alexander Davydov, Deniz Lapsekili +2
Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. We present Extended Line…
Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach
Saber Jafarpour, Alexander Davydov, Matthew Abate +2
This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks based upon non-Euclidean contraction theory. The…
Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks
Alexander Davydov, Saber Jafarpour, Matthew Abate +2
We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs). INNs are a class of implicit learning models that use implicit equations…
Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach
Saber Jafarpour, Matthew Abate, Alexander Davydov +2
Implicit neural networks are a general class of learning models that replace the layers in traditional feedforward models with implicit algebraic equations. Compared to traditional…
Robust Implicit Networks via Non-Euclidean Contractions
Saber Jafarpour, Alexander Davydov, Anton V. Proskurnikov +1
Implicit neural networks, a.k.a., deep equilibrium networks, are a class of implicit-depth learning models where function evaluation is performed by solving a fixed point equation.…