Auswahl der wissenschaftlichen Literatur zum Thema „Continuous and distributed machine learning“
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Zeitschriftenartikel zum Thema "Continuous and distributed machine learning"
Stan, Ioan-Mihail, Siarhei Padolski und Christopher Jon Lee. „Exploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3“. EPJ Web of Conferences 251 (2021): 02009. http://dx.doi.org/10.1051/epjconf/202125102009.
Der volle Inhalt der QuelleYin, Zhongdong, Jingjing Tu und Yonghai Xu. „Development of a Kernel Extreme Learning Machine Model for Capacity Selection of Distributed Generation Considering the Characteristics of Electric Vehicles“. Applied Sciences 9, Nr. 12 (13.06.2019): 2401. http://dx.doi.org/10.3390/app9122401.
Der volle Inhalt der QuelleBrophy, Eoin, Maarten De Vos, Geraldine Boylan und Tomás Ward. „Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach“. Sensors 21, Nr. 18 (21.09.2021): 6311. http://dx.doi.org/10.3390/s21186311.
Der volle Inhalt der QuelleVrachimis, Andreas, Stella Gkegka und Kostas Kolomvatsos. „Resilient edge machine learning in smart city environments“. Journal of Smart Cities and Society 2, Nr. 1 (07.07.2023): 3–24. http://dx.doi.org/10.3233/scs-230005.
Der volle Inhalt der QuelleMusa, M. O., und E. E. Odokuma. „A framework for the detection of distributed denial of service attacks on network logs using ML and DL classifiers“. Scientia Africana 22, Nr. 3 (25.01.2024): 153–64. http://dx.doi.org/10.4314/sa.v22i3.14.
Der volle Inhalt der QuelleOliveri, Giorgio, Lucas C. van Laake, Cesare Carissimo, Clara Miette und Johannes T. B. Overvelde. „Continuous learning of emergent behavior in robotic matter“. Proceedings of the National Academy of Sciences 118, Nr. 21 (10.05.2021): e2017015118. http://dx.doi.org/10.1073/pnas.2017015118.
Der volle Inhalt der QuelleKodaira, Daisuke, Kazuki Tsukazaki, Taiki Kure und Junji Kondoh. „Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations“. Energies 14, Nr. 21 (04.11.2021): 7340. http://dx.doi.org/10.3390/en14217340.
Der volle Inhalt der QuelleHua, Xia, und Lei Han. „Design and Practical Application of Sports Visualization Platform Based on Tracking Algorithm“. Computational Intelligence and Neuroscience 2022 (16.08.2022): 1–9. http://dx.doi.org/10.1155/2022/4744939.
Der volle Inhalt der QuelleRustam, Furqan, Muhammad Faheem Mushtaq, Ameer Hamza, Muhammad Shoaib Farooq, Anca Delia Jurcut und Imran Ashraf. „Denial of Service Attack Classification Using Machine Learning with Multi-Features“. Electronics 11, Nr. 22 (20.11.2022): 3817. http://dx.doi.org/10.3390/electronics11223817.
Der volle Inhalt der QuelleHuang, Leqi. „Problems, solutions and improvements on federated learning model“. Applied and Computational Engineering 22, Nr. 1 (23.10.2023): 183–86. http://dx.doi.org/10.54254/2755-2721/22/20231215.
Der volle Inhalt der QuelleDissertationen zum Thema "Continuous and distributed machine learning"
Armond, Kenneth C. Jr. „Distributed Support Vector Machine Learning“. ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/711.
Der volle Inhalt der QuelleAddanki, Ravichandra. „Learning generalizable device placement algorithms for distributed machine learning“. Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122746.
Der volle Inhalt der QuelleCataloged from PDF version of thesis.
Includes bibliographical references (pages 47-50).
We present Placeto, a reinforcement learning (RL) approach to efficiently find device placements for distributed neural network training. Unlike prior approaches that only find a device placement for a specific computation graph, Placeto can learn generalizable device placement policies that can be applied to any graph. We propose two key ideas in our approach: (1) we represent the policy as performing iterative placement improvements, rather than outputting a placement in one shot; (2) we use graph embeddings to capture relevant information about the structure of the computation graph, without relying on node labels for indexing. These ideas allow Placeto to train efficiently and generalize to unseen graphs. Our experiments show that Placeto requires up to 6.1 x fewer training steps to find placements that are on par with or better than the best placements found by prior approaches. Moreover, Placeto is able to learn a generalizable placement policy for any given family of graphs, which can then be used without any retraining to predict optimized placements for unseen graphs from the same family. This eliminates the large overhead incurred by prior RL approaches whose lack of generalizability necessitates re-training from scratch every time a new graph is to be placed.
by Ravichandra Addanki.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Johansson, Samuel, und Karol Wojtulewicz. „Machine learning algorithms in a distributed context“. Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148920.
Der volle Inhalt der QuelleKarimi, Ahmad Maroof. „Distributed Machine Learning Based Intrusion Detection System“. University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470401374.
Der volle Inhalt der QuelleZam, Anton. „Evaluating Distributed Machine Learning using IoT Devices“. Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42388.
Der volle Inhalt der QuelleInternet of things is growing every year with new devices being added all the time. Although some of the devices are continuously in use a large amount of them are mostly idle and sitting on untapped processing power that could be used to compute machine learning computations. There currently exist a lot of different methods to combine the processing power of multiple devices to compute machine learning task these are often called distributed machine learning methods. The main focus of this thesis is to evaluate these distributed machine learning methods to see if they could be implemented on IoT devices and if so, measure how efficient and scalable these methods are. The method chosen for implementation was called “MultiWorkerMirrorStrategy” and this method was evaluated by comparing the training time, training accuracy and evaluation accuracy of 2,3 and 4 Raspberry pi:s with a nondistributed machine learning method with 1 Raspberry pi. The results showed that although the computational power increased with every added device the training time increased while the rest of the measurements stayed the same. After the results were analyzed and discussed the conclusion of this were that the overhead added for communicating between devices were to high resulting in this method being very inefficient and wouldn’t scale without some sort of optimization being added.
Thompson, Simon Giles. „Distributed boosting algorithms“. Thesis, University of Portsmouth, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285529.
Der volle Inhalt der QuelleDahlberg, Leslie. „Evolutionary Computation in Continuous Optimization and Machine Learning“. Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35674.
Der volle Inhalt der QuelleOuyang, Hua. „Optimal stochastic and distributed algorithms for machine learning“. Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49091.
Der volle Inhalt der QuellePrueller, Hans. „Distributed online machine learning for mobile care systems“. Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10875.
Der volle Inhalt der QuelleKonečný, Jakub. „Stochastic, distributed and federated optimization for machine learning“. Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/31478.
Der volle Inhalt der QuelleBücher zum Thema "Continuous and distributed machine learning"
Weiss, Gerhard. Distributed machine learning. Sankt Augustin: Infix, 1995.
Den vollen Inhalt der Quelle findenTestas, Abdelaziz. Distributed Machine Learning with PySpark. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9751-3.
Der volle Inhalt der QuelleAmini, M. Hadi, Hrsg. Distributed Machine Learning and Computing. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-57567-9.
Der volle Inhalt der QuelleJiang, Jiawei, Bin Cui und Ce Zhang. Distributed Machine Learning and Gradient Optimization. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3420-8.
Der volle Inhalt der QuelleJoshi, Gauri. Optimization Algorithms for Distributed Machine Learning. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19067-4.
Der volle Inhalt der QuelleSahoo, Jyoti Prakash, Asis Kumar Tripathy, Manoranjan Mohanty, Kuan-Ching Li und Ajit Kumar Nayak, Hrsg. Advances in Distributed Computing and Machine Learning. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4807-6.
Der volle Inhalt der QuelleRout, Rashmi Ranjan, Soumya Kanti Ghosh, Prasanta K. Jana, Asis Kumar Tripathy, Jyoti Prakash Sahoo und Kuan-Ching Li, Hrsg. Advances in Distributed Computing and Machine Learning. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1018-0.
Der volle Inhalt der QuelleTripathy, Asis Kumar, Mahasweta Sarkar, Jyoti Prakash Sahoo, Kuan-Ching Li und Suchismita Chinara, Hrsg. Advances in Distributed Computing and Machine Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-4218-3.
Der volle Inhalt der QuelleNanda, Umakanta, Asis Kumar Tripathy, Jyoti Prakash Sahoo, Mahasweta Sarkar und Kuan-Ching Li, Hrsg. Advances in Distributed Computing and Machine Learning. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1841-2.
Der volle Inhalt der QuelleChinara, Suchismita, Asis Kumar Tripathy, Kuan-Ching Li, Jyoti Prakash Sahoo und Alekha Kumar Mishra, Hrsg. Advances in Distributed Computing and Machine Learning. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1203-2.
Der volle Inhalt der QuelleBuchteile zum Thema "Continuous and distributed machine learning"
Carter, Eric, und Matthew Hurst. „Continuous Delivery“. In Agile Machine Learning, 59–69. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5107-2_3.
Der volle Inhalt der QuelleYang, Qiang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen und Han Yu. „Distributed Machine Learning“. In Federated Learning, 33–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01585-4_3.
Der volle Inhalt der QuelleGalakatos, Alex, Andrew Crotty und Tim Kraska. „Distributed Machine Learning“. In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_80647-1.
Der volle Inhalt der QuelleGalakatos, Alex, Andrew Crotty und Tim Kraska. „Distributed Machine Learning“. In Encyclopedia of Database Systems, 1196–201. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_80647.
Der volle Inhalt der QuelleShultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond et al. „Continuous Attribute“. In Encyclopedia of Machine Learning, 226. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_172.
Der volle Inhalt der QuelleLi, Jin, Ping Li, Zheli Liu, Xiaofeng Chen und Tong Li. „Secure Distributed Learning“. In Privacy-Preserving Machine Learning, 47–56. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9139-3_4.
Der volle Inhalt der QuelleDucoulombier, Antoine, und Michèle Sebag. „Continuous mimetic evolution“. In Machine Learning: ECML-98, 334–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0026704.
Der volle Inhalt der QuelleCleophas, Ton J., und Aeilko H. Zwinderman. „Continuous Sequential Techniques“. In Machine Learning in Medicine, 187–94. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6886-4_18.
Der volle Inhalt der QuelleChen, Zhiyuan, und Bing Liu. „Continuous Knowledge Learning in Chatbots“. In Lifelong Machine Learning, 131–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01581-6_8.
Der volle Inhalt der QuelleLiu, Mark. „Q-Learning with Continuous States“. In Machine Learning, Animated, 285–300. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/b23383-15.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Continuous and distributed machine learning"
Belcastro, Loris, Fabrizio Marozzo, Aleandro Presta und Domenico Talia. „A Spark-based Task Allocation Solution for Machine Learning in the Edge-Cloud Continuum“. In 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), 576–82. IEEE, 2024. http://dx.doi.org/10.1109/dcoss-iot61029.2024.00090.
Der volle Inhalt der QuelleBarros, Claudio D. T., Daniel N. R. da Silva und Fabio A. M. Porto. „Machine Learning on Graph-Structured Data“. In Anais Estendidos do Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbbd_estendido.2021.18179.
Der volle Inhalt der QuelleChepurnov, A., und N. Ershov. „APPLICATION OF MACHINE LEARNING METHODS FOR CROSS-CLASSIFICATION OF ALGORITHMS AND PROBLEMS OF MULTIVARIATE CONTINUOUS OPTIMIZATION“. In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.67.50.001.
Der volle Inhalt der QuelleSartzetakis, Ippokratis, Polyzois Soumplis, Panagiotis Pantazopoulos, Konstantinos V. Katsaros, Vasilis Sourlas und Emmanouel Manos Varvarigos. „Resource Allocation for Distributed Machine Learning at the Edge-Cloud Continuum“. In ICC 2022 - IEEE International Conference on Communications. IEEE, 2022. http://dx.doi.org/10.1109/icc45855.2022.9838647.
Der volle Inhalt der QuelleTiezzi, Matteo, Simone Marullo, Lapo Faggi, Enrico Meloni, Alessandro Betti und Stefano Melacci. „Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams“. In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/483.
Der volle Inhalt der QuelleGupta, Sujasha, Srivatsava Krishnan und Vishnubaba Sundaresan. „Structural Health Monitoring of Composite Structures via Machine Learning of Mechanoluminescence“. In ASME 2019 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/smasis2019-5697.
Der volle Inhalt der QuelleMendoza, Alberto, Çağrı Cerrahoğlu, Alessandro Delfino und Martin Sundin. „Signal Processing and Machine Learning for Effective Integration of Distributed Fiber Optic Sensing Data in Production Petrophysics“. In 2022 SPWLA 63rd Annual Symposium. Society of Petrophysicists and Well Log Analysts, 2022. http://dx.doi.org/10.30632/spwla-2022-0016.
Der volle Inhalt der QuelleSadigov, Teymur, Cagri Cerrahoglu, James Ramsay, Laurence Burchell, Sean Cavalero, Thomas Watson, Pradyumna Thiruvenkatanathan und Martin Sundin. „Real-Time Water Injection Monitoring with Distributed Fiber Optics Using Physics-Informed Machine Learning“. In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/30982-ms.
Der volle Inhalt der Quelle„Session details: 1st Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC)“. In UCC '21: 2021 IEEE/ACM 14th International Conference on Utility and Cloud Computing, herausgegeben von Luiz F. Bittencourt, Ian Foster und Filip De Turck. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3517186.
Der volle Inhalt der QuelleGao, Hongchang, Hanzi Xu und Slobodan Vucetic. „Sample Efficient Decentralized Stochastic Frank-Wolfe Methods for Continuous DR-Submodular Maximization“. In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/482.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Continuous and distributed machine learning"
Shead, Timothy, Jonathan Berry, Cynthia Phillips und Jared Saia. Information-Theoretically Secure Distributed Machine Learning. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1763277.
Der volle Inhalt der QuelleLee, Ying-Ying, und Kyle Colangelo. Double debiased machine learning nonparametric inference with continuous treatments. The IFS, Oktober 2019. http://dx.doi.org/10.1920/wp.cem.2019.5419.
Der volle Inhalt der QuelleLee, Ying-Ying, und Kyle Colangelo. Double debiased machine learning nonparametric inference with continuous treatments. The IFS, Dezember 2019. http://dx.doi.org/10.1920/wp.cem.2019.7219.
Der volle Inhalt der QuelleHuang, Amy, Katelyn Barnes, Joseph Bearer, Evan Chrisinger und Christopher Stone. Integrating Distributed-Memory Machine Learning into Large-Scale HPC Simulations. Office of Scientific and Technical Information (OSTI), Mai 2018. http://dx.doi.org/10.2172/1460078.
Der volle Inhalt der QuelleVarastehpour, Soheil, Hamid Sharifzadeh und Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.
Der volle Inhalt der QuelleLiu, Xiaopei, Dan Liu und Cong’e Tan. Gut microbiome-based machine learning for diagnostic prediction of liver fibrosis and cirrhosis: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, Mai 2022. http://dx.doi.org/10.37766/inplasy2022.5.0133.
Der volle Inhalt der QuelleChoquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2019. http://dx.doi.org/10.55274/r0011568.
Der volle Inhalt der QuelleVisser, R., H. Kao, R. M. H. Dokht, A. B. Mahani und S. Venables. A comprehensive earthquake catalogue for northeastern British Columbia: the northern Montney trend from 2017 to 2020 and the Kiskatinaw Seismic Monitoring and Mitigation Area from 2019 to 2020. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329078.
Der volle Inhalt der QuelleHarris, L. B., P. Adiban und E. Gloaguen. The role of enigmatic deep crustal and upper mantle structures on Au and magmatic Ni-Cu-PGE-Cr mineralization in the Superior Province. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328984.
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