Imagen de Portada

Component-Aware Pruning Framework for Neural Network Controllers via Gradient-Based Importance Estimation

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Sundaram, Ganesh, Ulmen, Jonas, Görges, Daniel
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Systems and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis
por: Sundaram, Ganesh, et al.
Publicado: (2025)

Application-Specific Component-Aware Structured Pruning of Deep Neural Networks in Control via Soft Coefficient Optimization
por: Sundaram, Ganesh, et al.
Publicado: (2025)

COMponent-Aware Pruning for Accelerated Control Tasks in Latent Space Models
por: Sundaram, Ganesh, et al.
Publicado: (2025)

Learning State-Space Models of Dynamic Systems from Arbitrary Data using Joint Embedding Predictive Architectures
por: Ulmen, Jonas, et al.
Publicado: (2025)

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

A Latent Space Framework for Modeling Transient Engine Emissions Using Joint Embedding Predictive Architectures
por: Sundaram, Ganesh, et al.
Publicado: (2026)

FPGA-Based Neural Thrust Controller for UAVs
por: Azem, Sharif, et al.
Publicado: (2024)

Pontryagin Optimal Control via Neural Networks
por: Gu, Chengyang, et al.
Publicado: (2022)

Optimisation of Structured Neural Controller Based on Continuous-Time Policy Gradient
por: Cho, Namhoon, et al.
Publicado: (2022)

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

Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics
por: Frauenknecht, Bernd, et al.
Publicado: (2026)

Introducing a Deep Neural Network-based Model Predictive Control Framework for Rapid Controller Implementation
por: Gordon, David C., et al.
Publicado: (2023)

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

A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning
por: Chakrabarti, Kushal, et al.
Publicado: (2022)

Sample-Free Safety Assessment of Neural Network Controllers via Taylor Methods
por: Evans, Adam, et al.
Publicado: (2026)

Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks
por: Ito, Junsei, et al.
Publicado: (2025)

Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)
por: Braniff, Austin, et al.
Publicado: (2025)

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

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

Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation
por: Azimian, Behrouz, et al.
Publicado: (2023)

Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach
por: Song, Zihao, et al.
Publicado: (2025)

Safe and Robust Domains of Attraction for Discrete-Time Systems: A Set-Based Characterization and Certifiable Neural Network Estimation
por: Serry, Mohamed, et al.
Publicado: (2026)

Gradient Flow Equations for Deep Linear Neural Networks: A Survey from a Network Perspective
por: Wendin, Joel, et al.
Publicado: (2025)

The Silence that Speaks: Neural Estimation via Communication Gaps
por: Aggarwal, Shubham, et al.
Publicado: (2025)

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

A Physics-Informed Neural Networks-Based Model Predictive Control Framework for $SIR$ Epidemics
por: Zhong, Aiping, et al.
Publicado: (2025)

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

Efficient Interaction-Aware Interval Analysis of Neural Network Feedback Loops
por: Jafarpour, Saber, et al.
Publicado: (2023)

Dictionary-Learning-Based Data Pruning for System Identification
por: Wang, Tingna, et al.
Publicado: (2025)

Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification
por: Ferah, Mehmet Ali, et al.
Publicado: (2026)

Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies
por: Cao, John, et al.
Publicado: (2025)

Physics-Informed Neural Networks for Accelerating Power System State Estimation
por: Falas, Solon, et al.
Publicado: (2023)

Gradient Estimation and Variance Reduction in Stochastic and Deterministic Models
por: Keane, Ronan
Publicado: (2024)

PowerGNN: A Topology-Aware Graph Neural Network for Electricity Grids
por: Suri, Dhruv, et al.
Publicado: (2025)

Integrating Lagrangian Neural Networks into the Dyna Framework for Reinforcement Learning
por: Das, Shreya, et al.
Publicado: (2026)

Neural Network-Based Piecewise Survival Models
por: Holmer, Olov, et al.
Publicado: (2024)

Runtime Monitoring and Fault Detection for Neural Network-Controlled Systems
por: Lan, Jianglin, et al.
Publicado: (2024)

Synthesizing Neural Network Controllers with Closed-Loop Dissipativity Guarantees
por: Junnarkar, Neelay, et al.
Publicado: (2024)

Equivalent and Compact Representations of Neural Network Controllers With Decision Trees
por: Chang, Kevin, et al.
Publicado: (2023)

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