Copertina

Provable Bounds on the Hessian of Neural Networks: Derivative-Preserving Reachability Analysis

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Sharifi, Sina, Fazlyab, Mahyar
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Systems and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Sequential QCQP for Bilevel Optimization with Line Search
di: Sharifi, Sina, et al.
Pubblicazione: (2025)

Hierarchical End-to-End Taylor Bounds for Complete Neural Network Verification
di: Entesari, Taha, et al.
Pubblicazione: (2026)

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

Actor-Critic Physics-informed Neural Lyapunov Control
di: Wang, Jiarui, et al.
Pubblicazione: (2024)

Compositional Curvature Bounds for Deep Neural Networks
di: Entesari, Taha, et al.
Pubblicazione: (2024)

Generalized Model Predictive Path Integral Control as Expectation--Maximization
di: Wang, Jiarui, et al.
Pubblicazione: (2026)

Model Predictive Path Integral Control as Preconditioned Gradient Descent
di: Fazlyab, Mahyar, et al.
Pubblicazione: (2026)

Verification-Aided Learning of Neural Network Barrier Functions with Termination Guarantees
di: Chen, Shaoru, et al.
Pubblicazione: (2024)

Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuation
di: Manda, Lakshmideepakreddy, et al.
Pubblicazione: (2024)

Domain Adaptive Safety Filters via Deep Operator Learning
di: Manda, Lakshmideepakreddy, et al.
Pubblicazione: (2024)

Provably Bounding Neural Network Preimages
di: Kotha, Suhas, et al.
Pubblicazione: (2023)

Quadratic Characterizations for Reachability Analysis of Neural Networks
di: Khalife, Elias, et al.
Pubblicazione: (2026)

Efficient Reachability Analysis for Convolutional Neural Networks Using Hybrid Zonotopes
di: Zhang, Yuhao, et al.
Pubblicazione: (2025)

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

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

Provably-Stable Neural Network-Based Control of Nonlinear Systems
di: Li, Anran, et al.
Pubblicazione: (2025)

Anytime-Feasible First-Order Optimization via Safe Sequential QCQP
di: Wang, Jiarui, et al.
Pubblicazione: (2025)

Neural Network-assisted Interval Reachability for Systems with Control Barrier Function-Based Safe Controllers
di: Ajeyemi, Damola, et al.
Pubblicazione: (2025)

Mixed Monotonicity Reachability Analysis of Neural ODE: A Trade-Off Between Tightness and Efficiency
di: Sayed, Abdelrahman Sayed, et al.
Pubblicazione: (2025)

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

An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments
di: Bajcsy, Andrea, et al.
Pubblicazione: (2019)

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

Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagation
di: Hu, Hanjiang, et al.
Pubblicazione: (2024)

Constructive Lyapunov Functions via Topology-Preserving Neural Networks
di: Oh, Jaehong
Pubblicazione: (2025)

Data-Driven Reachability Analysis via Diffusion Models with PAC Guarantees
di: Huang, Yanliang, et al.
Pubblicazione: (2026)

Hessian-Informed Flow Matching
di: Sprague, Christopher Iliffe, et al.
Pubblicazione: (2024)

The Reachability Problem for Neural-Network Control Systems
di: Schilling, Christian, et al.
Pubblicazione: (2024)

State Derivative Normalization for Continuous-Time Deep Neural Networks
di: Weigand, Jonas, et al.
Pubblicazione: (2024)

$\texttt{immrax}$: A Parallelizable and Differentiable Toolbox for Interval Analysis and Mixed Monotone Reachability in JAX
di: Harapanahalli, Akash, et al.
Pubblicazione: (2024)

Convex Hulls of Reachable Sets
di: Lew, Thomas, et al.
Pubblicazione: (2023)

Safe LLM-Controlled Robots with Formal Guarantees via Reachability Analysis
di: Hafez, Ahmad, et al.
Pubblicazione: (2025)

Imitation-regularized Optimal Transport on Networks: Provable Robustness and Application to Logistics Planning
di: Oishi, Koshi, et al.
Pubblicazione: (2024)

Perturbed Gradient Descent via Convex Quadratic Approximation for Nonconvex Bilevel Optimization
di: Abolfazli, Nazanin, et al.
Pubblicazione: (2025)

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

Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations
di: Liu, Jun
Pubblicazione: (2026)

Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
di: Shen, Keyi, et al.
Pubblicazione: (2026)

Robust Verification of Controllers under State Uncertainty via Hamilton-Jacobi Reachability Analysis
di: Lin, Albert, et al.
Pubblicazione: (2025)

Symplectic Inductive Bias for Data-Driven Target Reachability in Hamiltonian Systems
di: Ouyang, Zhuo, et al.
Pubblicazione: (2026)

Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics
di: Prajapat, Manish, et al.
Pubblicazione: (2025)

Provably Efficient Sample Complexity for Robust CMDP
di: Ganguly, Sourav, et al.
Pubblicazione: (2025)