Copertina

Quantitative Flow Approximation Properties of Narrow Neural ODEs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Elamvazhuthi, Karthik
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Optimization and Control Machine Learning Systems and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

The Ground Cost for Optimal Transport of Angular Velocity
di: Elamvazhuthi, Karthik, et al.
Pubblicazione: (2025)

Score Matching Diffusion Based Feedback Control and Planning of Nonlinear Systems
di: Elamvazhuthi, Karthik, et al.
Pubblicazione: (2025)

Denoising Diffusion-Based Control of Nonlinear Systems
di: Elamvazhuthi, Karthik, et al.
Pubblicazione: (2024)

A Linear Test for Global Nonlinear Controllability
di: Elamvazhuthi, Karthik
Pubblicazione: (2024)

Geometry-Preserving Neural Architectures on Manifolds with Boundary
di: Elamvazhuthi, Karthik, et al.
Pubblicazione: (2026)

Expressivity of Quadratic Neural ODEs
di: Hanson, Joshua, et al.
Pubblicazione: (2025)

Uniform Sampling from the Reachable Set Using Optimal Transport
di: Elamvazhuthi, Karthik, et al.
Pubblicazione: (2025)

Density Stabilization Strategies for Nonholonomic Agents on Compact Manifolds
di: Elamvazhuthi, Karthik, et al.
Pubblicazione: (2023)

From NeurODEs to AutoencODEs: a mean-field control framework for width-varying Neural Networks
di: Cipriani, Cristina, et al.
Pubblicazione: (2023)

Certified Robust Invariant Polytope Training in Neural Controlled ODEs
di: Harapanahalli, Akash, et al.
Pubblicazione: (2024)

Rademacher Complexity of Neural ODEs via Chen-Fliess Series
di: Hanson, Joshua, et al.
Pubblicazione: (2024)

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

Flow Matching for Measure Transport and Feedback Stabilization of Control-Affine Systems
di: Elamvazhuthi, Karthik
Pubblicazione: (2025)

A minimax optimal control approach for robust neural ODEs
di: Cipriani, Cristina, et al.
Pubblicazione: (2023)

Neural Approximators for Low-Thrust Trajectory Transfer Cost and Reachability
di: Zhang, Zhong, et al.
Pubblicazione: (2025)

Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining
di: Hose, Henrik, et al.
Pubblicazione: (2024)

Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
di: Tabuada, Paulo, et al.
Pubblicazione: (2020)

Benamou-Brenier Formulation of Optimal Transport for Nonlinear Control Systems on Rd
di: Elamvazhuthi, Karthik
Pubblicazione: (2024)

Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow
di: Pineda, Salvador, et al.
Pubblicazione: (2024)

Predictor-Based Output-Feedback Control of Linear Systems with Time-Varying Input and Measurement Delays via Neural-Approximated Prediction Horizons
di: Bhan, Luke, et al.
Pubblicazione: (2026)

Random Features Approximation for Control-Affine Systems
di: Kazemian, Kimia, et al.
Pubblicazione: (2024)

On the Stability Connection Between Discrete-Time Algorithms and Their Resolution ODEs: Applications to Min-Max Optimisation
di: Farzin, Amir Ali, et al.
Pubblicazione: (2026)

Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem
di: Haque, Shaan Ul, et al.
Pubblicazione: (2024)

A Simple Finite-Time Analysis of TD Learning with Linear Function Approximation
di: Mitra, Aritra
Pubblicazione: (2024)

Approximate non-linear model predictive control with safety-augmented neural networks
di: Hose, Henrik, et al.
Pubblicazione: (2023)

Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow
di: Rosemberg, Andrew, et al.
Pubblicazione: (2025)

Safe Gradient Flow for Bilevel Optimization
di: Sharifi, Sina, et al.
Pubblicazione: (2025)

Certified Approximate Reachability (CARe): Formal Error Bounds on Deep Learning of Reachable Sets
di: Solanki, Prashant, et al.
Pubblicazione: (2025)

Approximation with Random Shallow ReLU Networks with Applications to Model Reference Adaptive Control
di: Lamperski, Andrew, et al.
Pubblicazione: (2024)

Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way
di: Kwon, Jeongyeol, et al.
Pubblicazione: (2024)

Control Disturbance Rejection in Neural ODEs
di: Bayram, Erkan, et al.
Pubblicazione: (2025)

Homotopy-based training of NeuralODEs for accurate dynamics discovery
di: Ko, Joon-Hyuk, et al.
Pubblicazione: (2022)

Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise
di: Haque, Shaan Ul, et al.
Pubblicazione: (2023)

Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis
di: Chandak, Siddharth, et al.
Pubblicazione: (2026)

$O(1/k)$ Finite-Time Bound for Non-Linear Two-Time-Scale Stochastic Approximation
di: Chandak, Siddharth
Pubblicazione: (2025)

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

Enhancing Convergence of Decentralized Gradient Tracking under the KL Property
di: Chen, Xiaokai, et al.
Pubblicazione: (2024)

Forward Invariance in Neural Network Controlled Systems
di: Harapanahalli, Akash, et al.
Pubblicazione: (2023)

Online Planning of Power Flows for Power Systems Against Bushfires Using Spatial Context
di: Xu, Jianyu, et al.
Pubblicazione: (2024)

On the Convergence of Overparameterized Problems: Inherent Properties of the Compositional Structure of Neural Networks
di: de Oliveira, Arthur Castello Branco, et al.
Pubblicazione: (2025)