Artículos de revistas sobre el tema "Dynamic optimal learning rate"
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Chinrungrueng, C. y C. H. Sequin. "Optimal adaptive k-means algorithm with dynamic adjustment of learning rate". IEEE Transactions on Neural Networks 6, n.º 1 (1995): 157–69. http://dx.doi.org/10.1109/72.363440.
Texto completoZhu, Yingqiu, Danyang Huang, Yuan Gao, Rui Wu, Yu Chen, Bo Zhang y Hansheng Wang. "Automatic, dynamic, and nearly optimal learning rate specification via local quadratic approximation". Neural Networks 141 (septiembre de 2021): 11–29. http://dx.doi.org/10.1016/j.neunet.2021.03.025.
Texto completoLeen, Todd K., Bernhard Schottky y David Saad. "Optimal asymptotic learning rate: Macroscopic versus microscopic dynamics". Physical Review E 59, n.º 1 (1 de enero de 1999): 985–91. http://dx.doi.org/10.1103/physreve.59.985.
Texto completoKalvit, Anand y Assaf Zeevi. "Dynamic Learning in Large Matching Markets". ACM SIGMETRICS Performance Evaluation Review 50, n.º 2 (30 de agosto de 2022): 18–20. http://dx.doi.org/10.1145/3561074.3561081.
Texto completoZheng, Jiangbo, Yanhong Gan, Ying Liang, Qingqing Jiang y Jiatai Chang. "Joint Strategy of Dynamic Ordering and Pricing for Competing Perishables with Q-Learning Algorithm". Wireless Communications and Mobile Computing 2021 (13 de marzo de 2021): 1–19. http://dx.doi.org/10.1155/2021/6643195.
Texto completoChen, Zhigang, Rongwei Xu y Yongxi Yi. "Dynamic Optimal Control of Transboundary Pollution Abatement under Learning-by-Doing Depreciation". Complexity 2020 (9 de junio de 2020): 1–17. http://dx.doi.org/10.1155/2020/3763684.
Texto completoDe, Shipra y Darryl A. Seale. "Dynamic Decision Making and Race Games". ISRN Operations Research 2013 (7 de agosto de 2013): 1–15. http://dx.doi.org/10.1155/2013/452162.
Texto completoYao, Yuhang y Carlee Joe-Wong. "Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 5 (18 de mayo de 2021): 4608–16. http://dx.doi.org/10.1609/aaai.v35i5.16590.
Texto completoLiu, Haijun. "A Study of an IT-Assisted Higher Education Model Based on Distributed Hardware-Assisted Tracking Intervention". Occupational Therapy International 2022 (8 de abril de 2022): 1–12. http://dx.doi.org/10.1155/2022/8862716.
Texto completoLi, Ao, Zhaoman Wan y Zhong Wan. "Optimal Design of Online Sequential Buy-Price Auctions with Consumer Valuation Learning". Asia-Pacific Journal of Operational Research 37, n.º 03 (29 de abril de 2020): 2050012. http://dx.doi.org/10.1142/s0217595920500128.
Texto completoWang, Xing-Ju, Xiao-Ming Xi y Gui-Feng Gao. "Reinforcement Learning Ramp Metering without Complete Information". Journal of Control Science and Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/208456.
Texto completoShi, Yuanji, Zhiwei Yuan, Xiaorong Zhu y Hongbo Zhu. "An Adaptive Routing Algorithm for Inter-Satellite Networks Based on the Combination of Multipath Transmission and Q-Learning". Processes 11, n.º 1 (5 de enero de 2023): 167. http://dx.doi.org/10.3390/pr11010167.
Texto completoMinghai Yuan, Minghai Yuan, Chenxi Zhang Minghai Yuan, Kaiwen Zhou Chenxi Zhang y Fengque Pei Kaiwen Zhou. "Real-time Allocation of Shared Parking Spaces Based on Deep Reinforcement Learning". 網際網路技術學刊 24, n.º 1 (enero de 2023): 035–43. http://dx.doi.org/10.53106/160792642023012401004.
Texto completoJepma, Marieke, Stephen B. R. E. Brown, Peter R. Murphy, Stephany C. Koelewijn, Boukje de Vries, Arn M. van den Maagdenberg y Sander Nieuwenhuis. "Noradrenergic and Cholinergic Modulation of Belief Updating". Journal of Cognitive Neuroscience 30, n.º 12 (diciembre de 2018): 1803–20. http://dx.doi.org/10.1162/jocn_a_01317.
Texto completoXiang, Yao, Jingling Yuan, Ruiqi Luo, Xian Zhong y Tao Li. "An Energy Dynamic Control Algorithm Based on Reinforcement Learning for Data Centers". International Journal of Pattern Recognition and Artificial Intelligence 33, n.º 13 (15 de diciembre de 2019): 1951009. http://dx.doi.org/10.1142/s0218001419510091.
Texto completoChiu, Kai-Cheng, Chien-Chang Liu y Li-Der Chou. "Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN". Wireless Communications and Mobile Computing 2022 (31 de enero de 2022): 1–16. http://dx.doi.org/10.1155/2022/1330993.
Texto completoDing, Fan, Yongyi Zhang, Rui Chen, Zhanwen Liu y Huachun Tan. "A Deep Learning Based Traffic State Estimation Method for Mixed Traffic Flow Environment". Journal of Advanced Transportation 2022 (7 de abril de 2022): 1–12. http://dx.doi.org/10.1155/2022/2166345.
Texto completoChan, Felix T. S., Zhengxu Wang, Yashveer Singh, X. P. Wang, J. H. Ruan y M. K. Tiwari. "Activity scheduling and resource allocation with uncertainties and learning in activities". Industrial Management & Data Systems 119, n.º 6 (8 de julio de 2019): 1289–320. http://dx.doi.org/10.1108/imds-01-2019-0002.
Texto completoStarling, Carlos, Jackson Machado-Pinto, Unaí Tupinambás, Estevão Urbano Silva y Bráulio R. G. M. Couto. "404. COVID-19 Normality Rate: Criteria for Optimal Time to Return to In-person Learning". Open Forum Infectious Diseases 8, Supplement_1 (1 de noviembre de 2021): S303—S304. http://dx.doi.org/10.1093/ofid/ofab466.605.
Texto completoThanh, Pham Duy, Tran Nhut Khai Hoan, Hoang Thi Huong Giang y Insoo Koo. "Cache-Enabled Data Rate Maximization for Solar-Powered UAV Communication Systems". Electronics 9, n.º 11 (20 de noviembre de 2020): 1961. http://dx.doi.org/10.3390/electronics9111961.
Texto completoWei, Kefeng, Lincong Zhang, Xin Jiang y Yi Guo. "Q -Learning-Based High Credibility and Stability Routing Algorithm for Internet of Medical Things". Wireless Communications and Mobile Computing 2020 (26 de diciembre de 2020): 1–10. http://dx.doi.org/10.1155/2020/8856271.
Texto completoCao, Huazhen, Chong Gao, Xuan He, Yang Li y Tao Yu. "Multi-Agent Cooperation Based Reduced-Dimension Q(λ) Learning for Optimal Carbon-Energy Combined-Flow". Energies 13, n.º 18 (14 de septiembre de 2020): 4778. http://dx.doi.org/10.3390/en13184778.
Texto completoRodriguez, Renato, Yan Wang, Joseph Ozanne, Dogan Sumer, Dimitar Filev y Damoon Soudbakhsh. "Adaptive Takeoff Maneuver Optimization of a Sailing Boat for America’s Cup". Journal of Sailing Technology 7, n.º 01 (17 de octubre de 2022): 88–103. http://dx.doi.org/10.5957/jst/2022.7.4.88.
Texto completoDE FRANCO, CARMINE, JOHANN NICOLLE y HUYÊN PHAM. "BAYESIAN LEARNING FOR THE MARKOWITZ PORTFOLIO SELECTION PROBLEM". International Journal of Theoretical and Applied Finance 22, n.º 07 (noviembre de 2019): 1950037. http://dx.doi.org/10.1142/s0219024919500377.
Texto completoWang, Yi y Junhai Sun. "Design and Implementation of Virtual Reality Interactive Product Software Based on Artificial Intelligence Deep Learning Algorithm". Advances in Multimedia 2022 (26 de abril de 2022): 1–7. http://dx.doi.org/10.1155/2022/9104743.
Texto completoShi, Junqing, Fengxiang Qiao, Qing Li, Lei Yu y Yongju Hu. "Application and Evaluation of the Reinforcement Learning Approach to Eco-Driving at Intersections under Infrastructure-to-Vehicle Communications". Transportation Research Record: Journal of the Transportation Research Board 2672, n.º 25 (1 de octubre de 2018): 89–98. http://dx.doi.org/10.1177/0361198118796939.
Texto completoZhang, Xiyue y Guiping Chen. "Machine Learning Model-Based English Project Learning and Functional Research". Wireless Communications and Mobile Computing 2022 (4 de abril de 2022): 1–11. http://dx.doi.org/10.1155/2022/1940375.
Texto completoChen, Jinyu, Ziqi Zhong, Qindi Feng y Lei Liu. "The Multimodal Emotion Information Analysis of E-Commerce Online Pricing in Electronic Word of Mouth". Journal of Global Information Management 30, n.º 11 (7 de abril de 2022): 1–17. http://dx.doi.org/10.4018/jgim.315322.
Texto completoZhou, Tao, Zengchuan Dong, Xiuxiu Chen y Qihua Ran. "Decision Support Model for Ecological Operation of Reservoirs Based on Dynamic Bayesian Network". Water 13, n.º 12 (14 de junio de 2021): 1658. http://dx.doi.org/10.3390/w13121658.
Texto completoLouta, M., P. Sarigiannidis, S. Misra, P. Nicopolitidis y G. Papadimitriou. "RLAM: A Dynamic and Efficient Reinforcement Learning-Based Adaptive Mapping Scheme in Mobile WiMAX Networks". Mobile Information Systems 10, n.º 2 (2014): 173–96. http://dx.doi.org/10.1155/2014/213056.
Texto completoOu, Minghui, Hua Wei, Yiyi Zhang y Jiancheng Tan. "A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers". Energies 12, n.º 6 (14 de marzo de 2019): 995. http://dx.doi.org/10.3390/en12060995.
Texto completoWang, Ziwei, Xin Wang, Yijie Tang, Ying Liu y Jun Hu. "Optimal Tracking Control of a Nonlinear Multiagent System Using Q-Learning via Event-Triggered Reinforcement Learning". Entropy 25, n.º 2 (5 de febrero de 2023): 299. http://dx.doi.org/10.3390/e25020299.
Texto completoWang, Huitao, Ruopeng Yang, Changsheng Yin, Xiaofei Zou y Xuefeng Wang. "Research on the Difficulty of Mobile Node Deployment’s Self-Play in Wireless Ad Hoc Networks Based on Deep Reinforcement Learning". Wireless Communications and Mobile Computing 2021 (9 de marzo de 2021): 1–13. http://dx.doi.org/10.1155/2021/4361650.
Texto completoSaleem, Muhammad, Yasir Saleem, H. M. Shahzad Asif y M. Saleem Mian. "Quality Enhanced Multimedia Content Delivery for Mobile Cloud with Deep Reinforcement Learning". Wireless Communications and Mobile Computing 2019 (18 de julio de 2019): 1–15. http://dx.doi.org/10.1155/2019/5038758.
Texto completoWang, Qiulin, Baole Tao, Fulei Han y Wenting Wei. "Extraction and Recognition Method of Basketball Players’ Dynamic Human Actions Based on Deep Learning". Mobile Information Systems 2021 (26 de junio de 2021): 1–6. http://dx.doi.org/10.1155/2021/4437146.
Texto completoMaldonado, Bryan P., Nan Li, Ilya Kolmanovsky y Anna G. Stefanopoulou. "Learning reference governor for cycle-to-cycle combustion control with misfire avoidance in spark-ignition engines at high exhaust gas recirculation–diluted conditions". International Journal of Engine Research 21, n.º 10 (26 de junio de 2020): 1819–34. http://dx.doi.org/10.1177/1468087420929109.
Texto completoMarsetič, Rok, Darja Šemrov y Marijan Žura. "Road Artery Traffic Light Optimization with Use of the Reinforcement Learning". PROMET - Traffic&Transportation 26, n.º 2 (26 de abril de 2014): 101–8. http://dx.doi.org/10.7307/ptt.v26i2.1318.
Texto completoMayer, Polina N., Victor V. Pogorelko, Dmitry S. Voronin y Alexander E. Mayer. "Spall Fracture of Solid and Molten Copper: Molecular Dynamics, Mechanical Model and Strain Rate Dependence". Metals 12, n.º 11 (3 de noviembre de 2022): 1878. http://dx.doi.org/10.3390/met12111878.
Texto completoYazid, Yassine, Antonio Guerrero-González, Imad Ez-Zazi, Ahmed El Oualkadi y Mounir Arioua. "A Reinforcement Learning Based Transmission Parameter Selection and Energy Management for Long Range Internet of Things". Sensors 22, n.º 15 (28 de julio de 2022): 5662. http://dx.doi.org/10.3390/s22155662.
Texto completoChang, Chung-Ho y Jen-Ming Chen. "Capacity Policy for an OEM under Production Ramp-Up and Demand Diffusion". Mathematical Problems in Engineering 2022 (26 de mayo de 2022): 1–22. http://dx.doi.org/10.1155/2022/9510184.
Texto completoLi, Shu, Jiong Yu, Xusheng Du, Yi Lu y Rui Qiu. "Fair Outlier Detection Based on Adversarial Representation Learning". Symmetry 14, n.º 2 (9 de febrero de 2022): 347. http://dx.doi.org/10.3390/sym14020347.
Texto completoZhang, Zhen y Dongqing Wang. "EAQR: A Multiagent Q-Learning Algorithm for Coordination of Multiple Agents". Complexity 2018 (28 de agosto de 2018): 1–14. http://dx.doi.org/10.1155/2018/7172614.
Texto completoKim, Sang-Ho, Deog-Yeong Park y Ki-Hoon Lee. "Hybrid Deep Reinforcement Learning for Pairs Trading". Applied Sciences 12, n.º 3 (18 de enero de 2022): 944. http://dx.doi.org/10.3390/app12030944.
Texto completoHoppe, David y Constantin A. Rothkopf. "Learning rational temporal eye movement strategies". Proceedings of the National Academy of Sciences 113, n.º 29 (5 de julio de 2016): 8332–37. http://dx.doi.org/10.1073/pnas.1601305113.
Texto completoAbdalla, Hemn Barzan, Awder M. Ahmed, Subhi R. M. Zeebaree, Ahmed Alkhayyat y Baha Ihnaini. "Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce". PeerJ Computer Science 8 (31 de marzo de 2022): e937. http://dx.doi.org/10.7717/peerj-cs.937.
Texto completoKhanh, Tran Trong, Tran Hoang Hai, Md Delowar Hossain y Eui-Nam Huh. "Fuzzy-Assisted Mobile Edge Orchestrator and SARSA Learning for Flexible Offloading in Heterogeneous IoT Environment". Sensors 22, n.º 13 (23 de junio de 2022): 4727. http://dx.doi.org/10.3390/s22134727.
Texto completoJegminat, Jannes, Simone Carlo Surace y Jean-Pascal Pfister. "Learning as filtering: Implications for spike-based plasticity". PLOS Computational Biology 18, n.º 2 (23 de febrero de 2022): e1009721. http://dx.doi.org/10.1371/journal.pcbi.1009721.
Texto completoHrizi, Olfa, Karim Gasmi, Ibtihel Ben Ltaifa, Hamoud Alshammari, Hanen Karamti, Moez Krichen, Lassaad Ben Ammar y Mahmood A. Mahmood. "Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model". Journal of Healthcare Engineering 2022 (21 de marzo de 2022): 1–13. http://dx.doi.org/10.1155/2022/8950243.
Texto completoZhang, Huanan y Stefanus Jasin. "Online Learning and Optimization of (Some) Cyclic Pricing Policies in the Presence of Patient Customers". Manufacturing & Service Operations Management 24, n.º 2 (marzo de 2022): 1165–82. http://dx.doi.org/10.1287/msom.2021.0979.
Texto completoZheng, Shaoxiong, Peng Gao, Weixing Wang y Xiangjun Zou. "A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network". Applied Sciences 12, n.º 13 (2 de julio de 2022): 6721. http://dx.doi.org/10.3390/app12136721.
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