Imagen de Portada

Optimal Potential Shaping on SE(3) via Neural ODEs on Lie Groups

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Wotte, Yannik P., Califano, Federico, Stramigioli, Stefano
Formato:	Preprint
Publicado:	2024
Materias:	Optimization and Control Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Model order reduction via Lie groups
por: Wotte, Yannik P., et al.
Publicado: (2025)

Expressivity of Quadratic Neural ODEs
por: Hanson, Joshua, et al.
Publicado: (2025)

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

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

Geometric Foundations of Tuning without Forgetting in Neural ODEs
por: Bayram, Erkan, et al.
Publicado: (2025)

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

Quantitative Flow Approximation Properties of Narrow Neural ODEs
por: Elamvazhuthi, Karthik
Publicado: (2025)

Learning Aligned Stability in Neural ODEs Reconciling Accuracy with Robustness
por: Luo, Chaoyang, et al.
Publicado: (2025)

Training Neural ODEs Using Fully Discretized Simultaneous Optimization
por: Shapovalova, Mariia, et al.
Publicado: (2025)

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

FxTS-Net: Fixed-Time Stable Learning Framework for Neural ODEs
por: Luo, Chaoyang, et al.
Publicado: (2024)

Stochastic Hessian Fittings with Lie Groups
por: Li, Xi-Lin
Publicado: (2024)

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

Stable Robot Motions on Manifolds: Learning Lyapunov-Constrained Neural Manifold ODEs
por: Boetius, David, et al.
Publicado: (2025)

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

Schrödinger Bridge Over A Compact Connected Lie Group
por: Mahmood, Hamza, et al.
Publicado: (2026)

Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs
por: Heiss, Jakob, et al.
Publicado: (2024)

Optimal Depth of Neural Networks
por: Qi, Qian
Publicado: (2025)

Riemannian Neural Optimal Transport
por: Micheli, Alessandro, et al.
Publicado: (2026)

Quantitative Convergences of Lie Group Momentum Optimizers
por: Kong, Lingkai, et al.
Publicado: (2024)

Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyond
por: Soma, Tasuku, et al.
Publicado: (2022)

Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
por: Chen, Hao, et al.
Publicado: (2026)

A Guaranteed-Stable Neural Network Approach for Optimal Control of Nonlinear Systems
por: Li, Anran, et al.
Publicado: (2025)

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

Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks
por: Sanguin, Gabriele, et al.
Publicado: (2025)

MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
por: Zhu, Yuchen, et al.
Publicado: (2025)

Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal?
por: Súkeník, Peter, et al.
Publicado: (2024)

Optimality-Informed Neural Networks for Solving Parametric Optimization Problems
por: Hoffmann, Matthias K., et al.
Publicado: (2025)

Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
por: Dai, Yongcheng, et al.
Publicado: (2023)

Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control: Penalty Approach
por: Feng, Lechen, et al.
Publicado: (2025)

Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control: Non-Penalty Approach
por: Feng, Lechen, et al.
Publicado: (2025)

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

Calibrating Neural Networks' parameters through Optimal Contraction in a Prediction Problem
por: Gonzalo, Valdes
Publicado: (2024)

Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification
por: Anderson, Brendon G., et al.
Publicado: (2021)

Learning to Optimally Dispatch Power: Performance on a Nation-Wide Real-World Dataset
por: Boero, Ignacio, et al.
Publicado: (2025)

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

Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization
por: Gong, Xindi, et al.
Publicado: (2026)

Unifying Distributionally Robust Optimization via Optimal Transport Theory
por: Blanchet, Jose, et al.
Publicado: (2023)

Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
por: Kamboj, Ankur, et al.
Publicado: (2026)

Langevin Dynamics: A Unified Perspective on Optimization via Lyapunov Potentials
por: Chen, August Y., et al.
Publicado: (2024)