Suchergebnisse - "stochastic gradient algorithms"

Adaptive Time Synchronization in Time Sensitive-Wireless Sensor Networks Based on Stochastic Gradient Algorithms Framework

Autoren: Ramadan Abdul-Rashid Kar Tim Chan Mohd Amiruddin Abd Rahman et al.

Quelle: Computer Modeling in Engineering & Sciences. 142:2585-2616

Stochastic Gradient Algorithms and Reinforcement Learning

Quelle: Partially Observed Markov Decision Processes ISBN: 9781009449441

Stochastic Gradient Algorithms. Convergence Analysis

Quelle: Partially Observed Markov Decision Processes ISBN: 9781009449441

Auxiliary model‐based recursive least squares and stochastic gradient algorithms and convergence analysis for feedback nonlinear output‐error systems: Auxiliary model-based recursive least squares and stochastic gradient algorithms and convergence analysis for feedback nonlinear output-error systems

Autoren: Guangqin Miao Dan Yang Feng Ding

Quelle: International Journal of Adaptive Control and Signal Processing. 38:3268-3289

Schlagwörter: least squares, 0209 industrial biotechnology, feedback nonlinear system, Systems theory, control, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, stochastic gradient, auxiliary model, convergence analysis

Dateibeschreibung: application/xml

The auxiliary model based hierarchical stochastic gradient algorithms and convergence analysis for feedback nonlinear systems using the multi-innovation identification theory

Autoren: Guangqin Miao Dan Yang Feiyan Chen et al.

Supervised Stochastic Gradient Algorithms for Multi-Trial Source Separation

Autoren: Mehta, Ronak Otto, Mateus Piovezan Stanis, Noah et al.

Schlagwörter: Machine Learning, FOS: Computer and information sciences, Machine Learning (stat.ML), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2508.20618

RETRACTED ARTICLE: Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient

Autoren: Antoine Godichon-Baggioni

Quelle: Statistics. 57:637-668

Multikernel Passive Stochastic Gradient Algorithms and Transfer Learning

Autoren: Vikram Krishnamurthy George Yin

Quelle: IEEE Transactions on Automatic Control. 67:1792-1805

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (stat.ML), Systems and Control (eess.SY), 02 engineering and technology, Electrical Engineering and Systems Science - Systems and Control, 01 natural sciences, Machine Learning (cs.LG), Statistics - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, 0101 mathematics

Zugangs-URL: http://arxiv.org/pdf/2008.10020
http://arxiv.org/abs/2008.10020
http://xplorestaging.ieee.org/ielx7/9/4601496/09428601.pdf?arnumber=9428601

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

Autoren: Chun Wei Xiao Zhang Ling Xu et al.

Quelle: International Journal of Robust and Nonlinear Control. 32:5534-5554

Schlagwörter: 0209 industrial biotechnology, Engineering design, Electrical engineering. Electronics Nuclear engineering, TA174, TK, Manufactures, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, TS

Dateibeschreibung: application/pdf; text

Zugangs-URL: https://strathprints.strath.ac.uk/80209/1/Wei_etal_IJRNC_2022_Overall_recursive_least_squares_and_overall_stochastic_gradient_algorithms_and_their_convergence.pdf

Hierarchical maximum likelihood generalized extended stochastic gradient algorithms for bilinear‐in‐parameter systems

Autoren: Haibo Liu Junwei Wang Xiangxiang Meng

Quelle: Optimal Control Applications and Methods. 43:402-417

Schlagwörter: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/oca.2813

Fractional-Based Stochastic Gradient Algorithms for Time-Delayed ARX Models

Autoren: Liuxiao Guo Tianyang Xu Jing Chen et al.

Quelle: Circuits, Systems, and Signal Processing. 41:1895-1912

Schlagwörter: 0209 industrial biotechnology, 02 engineering and technology

Zugangs-URL: https://link.springer.com/article/10.1007%2Fs00034-021-01874-8

Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics

Autoren: Negrea, Jeffrey Yang, Jun Feng, Haoyue et al.

Quelle: Negrea , J , Yang , J , Feng , H , Roy , D M & Huggins , J H 2023 ' Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics ' arXiv preprint .

Index Begriffe: stat.CO, cs.LG, stat.ME, stat.ML, workingPaper

URL: https://researchprofiles.ku.dk/da/publications/tuning-stochastic-gradient-algorithms-for-statistical-inference-via-largesample-asymptotics(051ecd25-619f-45e8-a623-9d260fe771ea).html
https://curis.ku.dk/ws/files/362463845/Tuning_Stochastic_Gradient_Algorithms.pdf

Two improved generalized extended stochastic gradient algorithms for CARARMA systems

Autoren: Lingling Lv Yulin Zhang Quanzhen Huang et al.

Quelle: Journal of the Franklin Institute. 361:107295

Schlagwörter: CARARMA, Estimation and detection in stochastic control theory, Applications of mathematical programming, multi-innovation identification method, GESG, moving data window, Stochastic learning and adaptive control

Dateibeschreibung: application/xml

Zugangs-URL: https://zbmath.org/7973152
https://doi.org/10.1016/j.jfranklin.2024.107295

Statement of Retraction: Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient

Quelle: Statistics. 58:1531-1531

The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems

Autoren: Tang, Junqi Egiazarian, Karen Davies, Mike et al.

Weitere Verfasser: Tang, Junqi Egiazarian, Karen Davies, Mike et al.

Quelle: Tang, B J, Egiazarian, K & Davies, M 2019, The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems. in 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019-Proceedings ., 18778660, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, United Kingdom, 12/05/19 . https://doi.org/10.1109/ICASSP.2019.8683368
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Schlagwörter: hyperparameter learning, Signal Processing, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, maximum likelihood, Electrical and Electronic Engineering, 113 Computer and information sciences, Software, sparsity-inducing norms

Dateibeschreibung: application/pdf; fulltext

Zugangs-URL: https://www.pure.ed.ac.uk/ws/files/206650698/icassp2019.pdf
https://ieeexplore.ieee.org/document/8683368
https://dblp.uni-trier.de/db/conf/icassp/icassp2019.html#TangED19
http://dblp.uni-trier.de/db/conf/icassp/icassp2019.html#TangED19
https://sigport.org/documents/limitation-and-practical-acceleration-stochastic-gradient-algorithms-inverse-problems
https://www.pure.ed.ac.uk/ws/files/206650698/icassp2019.pdf
https://hdl.handle.net/20.500.11820/5b0fd9f6-6c4a-40b0-bfc2-d366ece233a6
https://trepo.tuni.fi/handle/10024/134969

A Class of Diffusion Zero Attracting Stochastic Gradient Algorithms With Exponentiated Error Cost Functions

Autoren: Zhengyan Luo Haiquan Zhao Xiangping Zeng

Quelle: IEEE Access, Vol 8, Pp 4885-4894 (2020)

Schlagwörter: sparse system, Diffusion adaptive algorithms, 0202 electrical engineering, electronic engineering, information engineering, variable scaling factor, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, zero attracting, exponentiated error, TK1-9971

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/8948470/08937756.pdf
https://doaj.org/article/3a96062fac564b8cbed583e626fa79ea
https://dblp.uni-trier.de/db/journals/access/access8.html#LuoZZ20
https://ieeexplore.ieee.org/abstract/document/8937756

Convergence Analysis I: Stochastic Gradient Algorithms

Quelle: Inference and Learning from Data ISBN: 9781009218146

Some Stochastic Gradient Algorithms for Hammerstein Systems with Piecewise Linearity

Autoren: Yan Pu Yongqing Yang Jing Chen

Quelle: Circuits, Systems, and Signal Processing. 40:1635-1651

Schlagwörter: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://link.springer.com/article/10.1007/s00034-020-01554-z
https://doi.org/10.1007/s00034-020-01554-z
https://dblp.uni-trier.de/db/journals/cssp/cssp40.html#PuYC21

On the rates of convergence of parallelized averaged stochastic gradient algorithms

Autoren: Godichon-Baggioni, Antoine Saadane, Sofiane Laboratoire de Mathématiques de l'INSA de Rouen Normandie (LMI) et al.

Weitere Verfasser: Godichon-Baggioni, Antoine Saadane, Sofiane Laboratoire de Mathématiques de l'INSA de Rouen Normandie (LMI) et al.

Quelle: Statistics. 54:618-635

Schlagwörter: Asynchronous parallel optimization, Mathematics - Statistics Theory, Statistics Theory (math.ST), 01 natural sciences, [STAT] Statistics [stat], [STAT]Statistics [stat], Averaging, FOS: Mathematics, Distributed estimation, 0101 mathematics, Central Limit Theorem, Stochastic Gradient Descent

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/pdf/1710.07926
http://arxiv.org/abs/1710.07926
http://ui.adsabs.harvard.edu/abs/2017arXiv171007926G/abstract
https://www.tandfonline.com/doi/full/10.1080/02331888.2020.1764557
https://hal.science/hal-01620943v1
https://doi.org/10.1080/02331888.2020.1764557
https://hal.science/hal-01620943v1/document

On Distributed Stochastic Gradient Algorithms for Global Optimization

Autoren: Brian Swenson H. Vincent Poor Anirudh Sridhar

Quelle: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :8594-8598

Schlagwörter: FOS: Computer and information sciences, 0209 industrial biotechnology, 0203 mechanical engineering, Optimization and Control (math.OC), FOS: Mathematics, Computer Science - Multiagent Systems, 02 engineering and technology, Mathematics - Optimization and Control, Multiagent Systems (cs.MA)

Zugangs-URL: http://arxiv.org/pdf/1910.09587
http://arxiv.org/abs/1910.09587
https://collaborate.princeton.edu/en/publications/on-distributed-stochastic-gradient-algorithms-for-global-optimiza
https://pennstate.pure.elsevier.com/en/publications/on-distributed-stochastic-gradient-algorithms-for-global-optimiza
http://dblp.uni-trier.de/db/journals/corr/corr1910.html#abs-1910-09587
https://dblp.uni-trier.de/db/conf/icassp/icassp2020.html#SwensonSP20
https://ieeexplore.ieee.org/abstract/document/9054279
https://arxiv.org/abs/1910.09587