Zobrazeno 1 - 10
of 120
pro vyhledávání: '"Konečný, Jakub"'
Autor:
Konečný, Jakub
The thesis focuses on transformation of business from a natural person to a limited liability company, especially in terms of accounting and tax aspects. The theoretical part presents the basic aspects of business of natural persons and limited liabi
Externí odkaz:
http://www.nusl.cz/ntk/nusl-444241
A significant bottleneck in federated learning (FL) is the network communication cost of sending model updates from client devices to the central server. We present a comprehensive empirical study of the statistics of model updates in FL, as well as
Externí odkaz:
http://arxiv.org/abs/2201.02664
Autor:
Konečný, Jakub
This diploma thesis focuses on development of graphical user interface for managing IVR applications. The work is more software oriented, it analyzes current state of the iPBX product belonging to the IPEX a.s. company, describes used technologies an
Externí odkaz:
http://www.nusl.cz/ntk/nusl-413192
Autor:
Wang, Jianyu, Charles, Zachary, Xu, Zheng, Joshi, Gauri, McMahan, H. Brendan, Arcas, Blaise Aguera y, Al-Shedivat, Maruan, Andrew, Galen, Avestimehr, Salman, Daly, Katharine, Data, Deepesh, Diggavi, Suhas, Eichner, Hubert, Gadhikar, Advait, Garrett, Zachary, Girgis, Antonious M., Hanzely, Filip, Hard, Andrew, He, Chaoyang, Horvath, Samuel, Huo, Zhouyuan, Ingerman, Alex, Jaggi, Martin, Javidi, Tara, Kairouz, Peter, Kale, Satyen, Karimireddy, Sai Praneeth, Konecny, Jakub, Koyejo, Sanmi, Li, Tian, Liu, Luyang, Mohri, Mehryar, Qi, Hang, Reddi, Sashank J., Richtarik, Peter, Singhal, Karan, Smith, Virginia, Soltanolkotabi, Mahdi, Song, Weikang, Suresh, Ananda Theertha, Stich, Sebastian U., Talwalkar, Ameet, Wang, Hongyi, Woodworth, Blake, Wu, Shanshan, Yu, Felix X., Yuan, Honglin, Zaheer, Manzil, Zhang, Mi, Zhang, Tong, Zheng, Chunxiang, Zhu, Chen, Zhu, Wennan
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving f
Externí odkaz:
http://arxiv.org/abs/2107.06917
Autor:
Charles, Zachary, Konečný, Jakub
Publikováno v:
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021. PMLR: Volume 130
We study a family of algorithms, which we refer to as local update methods, generalizing many federated and meta-learning algorithms. We prove that for quadratic models, local update methods are equivalent to first-order optimization on a surrogate l
Externí odkaz:
http://arxiv.org/abs/2103.05032
Autor:
Rozboudová, Lenka, Konečný, Jakub
Předkládaná monografie je věnována vybraným aspektům výuky ruštiny jako dalšího cizího jazyka v českém jazykovém a vzdělávacím prostředí. V knize jsou rozebírána témata, která různým způsobem utvářejí proces učení se r
Autor:
Charles, Zachary, Konečný, Jakub
We study a family of algorithms, which we refer to as local update methods, that generalize many federated learning and meta-learning algorithms. We prove that for quadratic objectives, local update methods perform stochastic gradient descent on a su
Externí odkaz:
http://arxiv.org/abs/2007.00878
Autor:
Reddi, Sashank, Charles, Zachary, Zaheer, Manzil, Garrett, Zachary, Rush, Keith, Konečný, Jakub, Kumar, Sanjiv, McMahan, H. Brendan
Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. Standard federated optimization methods such as Federated Aver
Externí odkaz:
http://arxiv.org/abs/2003.00295
Autor:
Kairouz, Peter, McMahan, H. Brendan, Avent, Brendan, Bellet, Aurélien, Bennis, Mehdi, Bhagoji, Arjun Nitin, Bonawitz, Kallista, Charles, Zachary, Cormode, Graham, Cummings, Rachel, D'Oliveira, Rafael G. L., Eichner, Hubert, Rouayheb, Salim El, Evans, David, Gardner, Josh, Garrett, Zachary, Gascón, Adrià, Ghazi, Badih, Gibbons, Phillip B., Gruteser, Marco, Harchaoui, Zaid, He, Chaoyang, He, Lie, Huo, Zhouyuan, Hutchinson, Ben, Hsu, Justin, Jaggi, Martin, Javidi, Tara, Joshi, Gauri, Khodak, Mikhail, Konečný, Jakub, Korolova, Aleksandra, Koushanfar, Farinaz, Koyejo, Sanmi, Lepoint, Tancrède, Liu, Yang, Mittal, Prateek, Mohri, Mehryar, Nock, Richard, Özgür, Ayfer, Pagh, Rasmus, Raykova, Mariana, Qi, Hang, Ramage, Daniel, Raskar, Ramesh, Song, Dawn, Song, Weikang, Stich, Sebastian U., Sun, Ziteng, Suresh, Ananda Theertha, Tramèr, Florian, Vepakomma, Praneeth, Wang, Jianyu, Xiong, Li, Xu, Zheng, Yang, Qiang, Yu, Felix X., Yu, Han, Zhao, Sen
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data d
Externí odkaz:
http://arxiv.org/abs/1912.04977
Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud. Existing w
Externí odkaz:
http://arxiv.org/abs/1912.00131