Cover Image

R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Barbara, Nicholas H., Wang, Ruigang, Manchester, Ian R.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Systems and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
by: Barbara, Nicholas H., et al.
Published: (2024)

Robustly Invertible Nonlinear Dynamics and the BiLipREN: Contracting Neural Models with Contracting Inverses
by: Zhang, Yurui, et al.
Published: (2025)

React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN
by: Barbara, Nicholas H., et al.
Published: (2025)

Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian
by: Pauli, Patricia, et al.
Published: (2023)

LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
by: Pauli, Patricia, et al.
Published: (2024)

RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
by: Barbara, Nicholas H., et al.
Published: (2023)

Negative Imaginary Neural ODEs: Learning to Control Mechanical Systems with Stability Guarantees
by: Shi, Kanghong, et al.
Published: (2025)

Learning Stable and Passive Neural Differential Equations
by: Cheng, Jing, et al.
Published: (2024)

Monotone, Bi-Lipschitz, and Polyak-Lojasiewicz Networks
by: Wang, Ruigang, et al.
Published: (2024)

Improved Scalable Lipschitz Bounds for Deep Neural Networks
by: Syed, Usman, et al.
Published: (2025)

Remarks on Lipschitz-Minimal Interpolation: Generalization Bounds and Neural Network Implementation
by: de Oliveira, Arthur C. B., et al.
Published: (2026)

LDLT $\mathcal{L}$-Lipschitz Network: Generalized Deep End-To-End Lipschitz Network Construction
by: Juston, Marius F. R., et al.
Published: (2025)

Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms
by: Martin, Andrea, et al.
Published: (2025)

Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
by: Liu, Dechuan, et al.
Published: (2026)

Lipschitz-Based Robustness Certification for Recurrent Neural Networks via Convex Relaxation
by: Hamelbeck, Paul, et al.
Published: (2025)

Learning Stable and Robust Linear Parameter-Varying State-Space Models
by: Verhoek, Chris, et al.
Published: (2023)

ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks
by: Xu, Yuezhu, et al.
Published: (2024)

Input Convex Lipschitz Recurrent Neural Networks for Robust and Efficient Process Modeling and Optimization
by: Wang, Zihao, et al.
Published: (2024)

Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise
by: Chandak, Siddharth, et al.
Published: (2025)

Contraction-Guided Adaptive Partitioning for Reachability Analysis of Neural Network Controlled Systems
by: Harapanahalli, Akash, et al.
Published: (2023)

High-Dimensional Surrogate Modeling for Closed-Loop Learning of Neural-Network-Parameterized Model Predictive Control
by: Hirt, Sebastian, et al.
Published: (2025)

Norm-Bounded Low-Rank Adaptation
by: Wang, Ruigang, et al.
Published: (2025)

Recurrent Auto-Encoders for Enhanced Deep Reinforcement Learning in Wilderness Search and Rescue Planning
by: Ewers, Jan-Hendrik, et al.
Published: (2025)

Stability and Performance Analysis of Discrete-Time ReLU Recurrent Neural Networks
by: Noori, Sahel Vahedi, et al.
Published: (2024)

A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
by: Gokhale, Anand, et al.
Published: (2026)

Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks
by: Wei, Lai, et al.
Published: (2021)

QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers
by: Chen, Haozhe, et al.
Published: (2024)

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems
by: Ashraf, Inaam, et al.
Published: (2025)

Physics-Informed Deep B-Spline Networks
by: Wang, Zhuoyuan, et al.
Published: (2025)

A Dynamic Recurrent Adjacency Memory Network for Mixed-Generation Power System Stability Forecasting
by: Ooi, Guang An, et al.
Published: (2025)

Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioning
by: Davydov, Alexander
Published: (2025)

Spectral Normalization for Lipschitz-Constrained Policies on Learning Humanoid Locomotion
by: Shin, Jaeyong, et al.
Published: (2025)

Tree Meets Transformer: A Hybrid Architecture for Scalable Power Allocation in Cell-Free Networks
by: Chafaa, Irched, et al.
Published: (2025)

Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUs
by: Kundacina, Ognjen, et al.
Published: (2023)

Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems
by: Li, Haoyu, et al.
Published: (2025)

Transformer-Based Scalable Multi-Agent Reinforcement Learning for Networked Systems with Long-Range Interactions
by: Sinha, Vidur, et al.
Published: (2025)

Absolute Stability of Nonlinear Negative Imaginary Systems with Application to Potential Energy Shaping
by: Shi, Kanghong, et al.
Published: (2026)

Bandit Algorithms for Deep Brain Stimulation
by: Gupta, Arkaprava, et al.
Published: (2026)

Controller Design for Structured State-space Models via Contraction Theory
by: Zakwan, Muhammad, et al.
Published: (2026)

Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems
by: D'Silva, Rihan Aaron, et al.
Published: (2026)