Copertina

Deep Learning Model Predictive Control for Deep Brain Stimulation in Parkinson's Disease

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Steffen, Sebastian, Cannon, Mark
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Optimization and Control Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Deep Operator Neural Network Model Predictive Control
di: de Jong, Thomas Oliver, et al.
Pubblicazione: (2025)

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
di: Peitz, Sebastian, et al.
Pubblicazione: (2024)

Sample-Efficient Reinforcement Learning Controller for Deep Brain Stimulation in Parkinson's Disease
di: Ravivarapu, Harsh, et al.
Pubblicazione: (2025)

An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
di: Hua, Mengjian, et al.
Pubblicazione: (2024)

Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control
di: Kato, Teruki, et al.
Pubblicazione: (2025)

Learning Exactly Linearizable Deep Dynamics Models
di: Moriyasu, Ryuta, et al.
Pubblicazione: (2023)

Training Deep Learning Models with Norm-Constrained LMOs
di: Pethick, Thomas, et al.
Pubblicazione: (2025)

Sparse Deep Learning Models with the $\ell_1$ Regularization
di: Shen, Lixin, et al.
Pubblicazione: (2024)

Deep learning adaptive Model Predictive Control of Fed-Batch Cultivations
di: Krausch, Niels, et al.
Pubblicazione: (2025)

Optimal Design of Volt/VAR Control Rules of Inverters using Deep Learning
di: Gupta, Sarthak, et al.
Pubblicazione: (2022)

Mathematical Foundations of Deep Learning
di: Ye, Xiaojing
Pubblicazione: (2026)

Fill-and-Spill: Deep Reinforcement Learning Policy Gradient Methods for Reservoir Operation Decision and Control
di: Tabas, Sadegh Sadeghi, et al.
Pubblicazione: (2024)

Deep Learning for the Multiple Optimal Stopping Problem
di: Laurière, Mathieu, et al.
Pubblicazione: (2025)

Deep Generative Demand Learning for Newsvendor and Pricing
di: Gong, Shijin, et al.
Pubblicazione: (2024)

Learning to Stop: Deep Learning for Mean Field Optimal Stopping
di: Magnino, Lorenzo, et al.
Pubblicazione: (2024)

Structure-Informed Deep Reinforcement Learning for Inventory Management
di: Maggiar, Alvaro, et al.
Pubblicazione: (2025)

Deep Reinforcement Learning: A Convex Optimization Approach
di: Gattami, Ather
Pubblicazione: (2024)

Learning Constrained Optimization with Deep Augmented Lagrangian Methods
di: Kotary, James, et al.
Pubblicazione: (2024)

Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
di: Tran, Hoang, et al.
Pubblicazione: (2024)

Position: Adopt Constraints Over Fixed Penalties in Deep Learning
di: Ramirez, Juan, et al.
Pubblicazione: (2025)

DLMMPR:Deep Learning-based Measurement Matrix for Phase Retrieval
di: Liu, Jing, et al.
Pubblicazione: (2025)

When Deep Learning Meets Polyhedral Theory: A Survey
di: Huchette, Joey, et al.
Pubblicazione: (2023)

Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations
di: Mahmoudinazlou, Sasan, et al.
Pubblicazione: (2024)

Enhancing Accuracy in Deep Learning Using Random Matrix Theory
di: Berlyand, Leonid, et al.
Pubblicazione: (2023)

Convergence Analysis for Learning Orthonormal Deep Linear Neural Networks
di: Qin, Zhen, et al.
Pubblicazione: (2023)

A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events
di: Taboga, Vincent, et al.
Pubblicazione: (2023)

Differentiable Model Predictive Control on the GPU
di: Adabag, Emre, et al.
Pubblicazione: (2025)

QLABGrad: a Hyperparameter-Free and Convergence-Guaranteed Scheme for Deep Learning
di: Fu, Minghan, et al.
Pubblicazione: (2023)

Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
di: Ding, Kuangyu, et al.
Pubblicazione: (2023)

EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization
di: Yau, Chung-Yiu, et al.
Pubblicazione: (2026)

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

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling
di: Rosso, Haley, et al.
Pubblicazione: (2025)

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
di: Xie, Xingyu, et al.
Pubblicazione: (2022)

Adaptive Moment Estimation Optimization Algorithm Using Projection Gradient for Deep Learning
di: Li, Yongqi, et al.
Pubblicazione: (2025)

Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes
di: Zhang, Erica, et al.
Pubblicazione: (2024)

Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces
di: Angiuli, Andrea, et al.
Pubblicazione: (2023)

CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
di: Feng, Miria, et al.
Pubblicazione: (2024)

Learning Fair Policies for Infectious Diseases Mitigation using Path Integral Control
di: Jia, Zhuangzhuang, et al.
Pubblicazione: (2025)

Natural Geometry of Robust Data Attribution: From Convex Models to Deep Networks
di: Li, Shihao, et al.
Pubblicazione: (2025)

Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization
di: Kovalev, Dmitry
Pubblicazione: (2025)