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06/22/2023
RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
Neural networks are typically sensitive to small input perturbations, le...
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04/12/2023
Learning Over All Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems
This paper presents a policy parameterization for learning-based control...
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04/06/2023
Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations
In this work, we introduce and study a class of Deep Neural Networks (DN...
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03/20/2023
Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian
We establish a layer-wise parameterization for 1D convolutional neural n...
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01/27/2023
Direct Parameterization of Lipschitz-Bounded Deep Networks
This paper introduces a new parameterization of deep neural networks (bo...
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12/23/2021
Globally convergent visual-feature range estimation with biased inertial measurements
The design of a globally convergent position observer for feature points...
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12/08/2021
Learning over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System
We propose a parameterization of nonlinear output feedback controllers f...
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12/02/2021
Youla-REN: Learning Nonlinear Feedback Policies with Robust Stability Guarantees
This paper presents a parameterization of nonlinear controllers for unce...
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10/13/2021
Learning Stable Koopman Embeddings
In this paper, we present a new data-driven method for learning stable m...
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07/29/2021
Distributed Identification of Contracting and/or Monotone Network Dynamics
This paper proposes methods for identification of large-scale networked ...
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04/13/2021
Recurrent Equilibrium Networks: Unconstrained Learning of Stable and Robust Dynamical Models
This paper introduces recurrent equilibrium networks (RENs), a new class...
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10/05/2020
Lipschitz Bounded Equilibrium Networks
This paper introduces new parameterizations of equilibrium neural networ...
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04/11/2020
Convex Sets of Robust Recurrent Neural Networks
Recurrent neural networks (RNNs) are a class of nonlinear dynamical syst...
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12/22/2019
Contracting Implicit Recurrent Neural Networks: Stable Models with Improved Trainability
Stability of recurrent models is closely linked with trainability, gener...
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03/27/2019
Priority Maps for Surveillance and Intervention of Wildfires and other Spreading Processes
Unmanned Aerial Vehicle (UAV) path planning algorithms often assume a kn...
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03/02/2018
