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Artykuły w czasopismach na temat "SGD asynchrone"
Lyngdoh, N., R. P. Gunaga, Geeta Joshi, R. Vasudeva, G. Ravikanth i R. Uma Shaanker. "Influence of geographic distance and genetic dissimilarity among clones on flowering synchrony in a Teak (Tectona grandis Linn. f) clonal seed orchard". Silvae Genetica 61, nr 1-6 (1.12.2012): 10–18. http://dx.doi.org/10.1515/sg-2012-0002.
Pełny tekst źródłaChen, Hao, Changmiao Tan, Huanhua Peng, Wentao Yang i Lelin Li. "A Qualitative Study of Water Quality Using Landsat 8 and Station Water Quality-Monitoring Data to Support SDG 6.3.2 Evaluations: A Case Study of Deqing, China". Water 16, nr 10 (7.05.2024): 1319. http://dx.doi.org/10.3390/w16101319.
Pełny tekst źródłaAlberio, A., S. Garibaldi, G. Mirizzi, E. Franchi, C. Viacava, G. Santoro, M. Piacenti, A. Rossi i N. Assanta. "P162 ARRHYTHMIC MITRAL VALVE PROLAPSE OCCURRING AS ABORTED SUDDEN CARDIAC DEATH". European Heart Journal Supplements 25, Supplement_D (maj 2023): D103. http://dx.doi.org/10.1093/eurheartjsupp/suad111.242.
Pełny tekst źródłaSarbunan, Thobias. "DOCUMENTARY OF COVID-19 EDUCATION JOURNEY". Al'Adzkiya International of Education and Sosial (AIoES) Journal 2, nr 1 (24.04.2021): 30–47. http://dx.doi.org/10.55311/aioes.v2i1.102.
Pełny tekst źródłaSun, Jianhui, Ying Yang, Guangxu Xun i Aidong Zhang. "Scheduling Hyperparameters to Improve Generalization: From Centralized SGD to Asynchronous SGD". ACM Transactions on Knowledge Discovery from Data, 22.06.2022. http://dx.doi.org/10.1145/3544782.
Pełny tekst źródłaRozprawy doktorskie na temat "SGD asynchrone"
Candela, Rosa. "Robust and scalable probabilistic machine learning methods with applications to the airline industry". Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS078.
Pełny tekst źródłaIn the airline industry, price prediction plays a significant role both for customers and travel companies. The former are interested in knowing the price evolution to get the cheapest ticket, the latter want to offer attractive tour packages and maximize their revenue margin. In this work we introduce some practical approaches to help travelers in dealing with uncertainty in ticket price evolution and we propose a data-driven framework to monitor time-series forecasting models' performance. Stochastic Gradient Descent (SGD) represents the workhorse optimization method in the field of machine learning and this is true also for distributed systems, which in last years are increasingly used for complex models trained on massive datasets. In asynchronous systems workers can use stale versions of the parameters, which slows SGD convergence. In this thesis we fill the gap in the literature and study sparsification methods in asynchronous settings. We provide a concise convergence rate analysis when the joint effects of sparsification and asynchrony are taken into account, and show that sparsified SGD converges at the same rate of standard SGD. Recently, SGD has played an important role also as a way to perform approximate Bayesian Inference. Stochastic gradient MCMC algorithms use indeed SGD with constant learning rate to obtain samples from the posterior distribution. Despite some promising results restricted to simple models, most of the existing works fall short in easily dealing with the complexity of the loss landscape of deep models. In this thesis we introduce a practical approach to posterior sampling, which requires weaker assumptions than existing algorithms
Ouquelle, Hassan. "Conception et validation d'un modèle de simulation de la machine asynchrone monophasée /". Thèse, Montréal : École de technologie supérieure, 2005. http://proquest.umi.com/pqdweb?did=1459901331&sid=3&Fmt=2&clientId=46962&RQT=309&VName=PQD.
Pełny tekst źródła"Mémoire présenté à l'École de technologie supérieure comme exigence partielle à l'obtention de la maîtrise en génie électrique". CaQMUQET Bibliogr. : f. [97]. Également disponible en version électronique. CaQMUQET
Streszczenia konferencji na temat "SGD asynchrone"
Regatti, Jayanth, Gaurav Tendolkar, Yi Zhou, Abhishek Gupta i Yingbin Liang. "Distributed SGD Generalizes Well Under Asynchrony". W 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2019. http://dx.doi.org/10.1109/allerton.2019.8919791.
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