Image de couverture de livre

Identifiable Representation and Model Learning for Latent Dynamic Systems

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zhang, Congxi, Xie, Yongchun
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Systems and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Tracking control of latent dynamic systems with application to spacecraft attitude control
par: Zhang, Congxi, et autres
Publié: (2024)

Identifiability of Differential-Algebraic Systems
par: Montanari, Arthur N., et autres
Publié: (2024)

Identifying Large-Scale Linear Parameter Varying Systems with Dynamic Mode Decomposition Methods
par: Jordanou, Jean Panaioti, et autres
Publié: (2025)

Barrier Certificates for Unknown Systems with Latent States and Polynomial Dynamics using Bayesian Inference
par: Lefringhausen, Robert, et autres
Publié: (2025)

Adaptive Federated Learning via Dynamical System Model
par: Agarwal, Aayushya, et autres
Publié: (2025)

Safely Learning Dynamical Systems
par: Ahmadi, Amir Ali, et autres
Publié: (2023)

Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States
par: Lefringhausen, Robert, et autres
Publié: (2023)

FedECADO: A Dynamical System Model of Federated Learning
par: Agarwal, Aayushya, et autres
Publié: (2024)

Learning Dissipative Neural Dynamical Systems
par: Xu, Yuezhu, et autres
Publié: (2023)

Slack More, Predict Better: Proximal Relaxation for Probabilistic Latent Variable Model-based Soft Sensors
par: Zou, Zehua, et autres
Publié: (2026)

FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning
par: Li, Chenhao, et autres
Publié: (2024)

Explore-then-Commit for Nonstationary Linear Bandits with Latent Dynamics
par: Choi, Sunmook, et autres
Publié: (2025)

Probabilistic Latent Variable Modeling for Dynamic Friction Identification and Estimation
par: Vantilborgh, Victor, et autres
Publié: (2024)

Learning Dynamical Systems by Leveraging Data from Similar Systems
par: Xin, Lei, et autres
Publié: (2023)

Efficient Neural Hybrid System Learning and Transition System Abstraction for Dynamical Systems
par: Yang, Yejiang, et autres
Publié: (2024)

Identifying Systems with Symmetries using Equivariant Autoregressive Reservoir Computers
par: Vides, Fredy, et autres
Publié: (2023)

Federated Causal Representation Learning in State-Space Systems for Decentralized Counterfactual Reasoning
par: Mohamed, Nazal, et autres
Publié: (2026)

Deep Learning of Dynamic Systems using System Identification Toolbox(TM)
par: Dai, Tianyu, et autres
Publié: (2024)

Diffeomorphic Obstacle Avoidance for Contractive Dynamical Systems via Implicit Representations
par: Simmoteit, Ken-Joel, et autres
Publié: (2025)

Filtering Jump Markov Systems with Partially Known Dynamics: A Model-Based Deep Learning Approach
par: Stamatelis, George, et autres
Publié: (2025)

Joint Learning of Linear Time-Invariant Dynamical Systems
par: Modi, Aditya, et autres
Publié: (2021)

Learning Exactly Linearizable Deep Dynamics Models
par: Moriyasu, Ryuta, et autres
Publié: (2023)

Transformers as Implicit State Estimators: In-Context Learning in Dynamical Systems
par: Akram, Usman, et autres
Publié: (2024)

Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds
par: Zhang, Yuyang, et autres
Publié: (2024)

A Transition System Abstraction Framework for Neural Network Dynamical System Models
par: Yang, Yejiang, et autres
Publié: (2024)

LUCID: Learning-Enabled Uncertainty-Aware Certification of Stochastic Dynamical Systems
par: Casablanca, Ernesto, et autres
Publié: (2025)

Adversarial Imitation Learning from Visual Observations using Latent Information
par: Giammarino, Vittorio, et autres
Publié: (2023)

CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing
par: Ghahramani, Mohammadhossein, et autres
Publié: (2026)

Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part I
par: Tian, Yi, et autres
Publié: (2022)

Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part II
par: Tian, Yi, et autres
Publié: (2026)

Learning System Dynamics without Forgetting
par: Zhang, Xikun, et autres
Publié: (2024)

Learning of Linear Dynamical Systems as a Non-Commutative Polynomial Optimization Problem
par: Zhou, Quan, et autres
Publié: (2020)

Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction
par: Cho, Namhoon, et autres
Publié: (2022)

Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
par: van Otterdijk, G. J. E., et autres
Publié: (2024)

A Data-Integrated Framework for Learning Fractional-Order Nonlinear Dynamical Systems
par: Yaghooti, Bahram, et autres
Publié: (2025)

Online Optimization and Ambiguity-based Learning of Distributionally Uncertain Dynamic Systems
par: Li, Dan, et autres
Publié: (2021)

Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control
par: Lee, Bruce D., et autres
Publié: (2024)

Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction
par: Kang, Beomseok, et autres
Publié: (2024)

A Unified Approach for Learning the Dynamics of Power System Generators and Inverter-based Resources
par: Liu, Shaohui, et autres
Publié: (2024)

Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document
par: Yang, Zewen, et autres
Publié: (2024)