Auswahl der wissenschaftlichen Literatur zum Thema „Multiple Aggregation Learning“
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Zeitschriftenartikel zum Thema "Multiple Aggregation Learning"
JIANG, JU, MOHAMED S. KAMEL und LEI CHEN. „AGGREGATION OF MULTIPLE REINFORCEMENT LEARNING ALGORITHMS“. International Journal on Artificial Intelligence Tools 15, Nr. 05 (Oktober 2006): 855–61. http://dx.doi.org/10.1142/s0218213006002990.
Der volle Inhalt der QuelleAydin, Bahadir, Yavuz Selim Yilmaz Yavuz Selim Yilmaz, Yaliang Li, Qi Li, Jing Gao und Murat Demirbas. „Crowdsourcing for Multiple-Choice Question Answering“. Proceedings of the AAAI Conference on Artificial Intelligence 28, Nr. 2 (27.07.2014): 2946–53. http://dx.doi.org/10.1609/aaai.v28i2.19016.
Der volle Inhalt der QuelleSinnott, Jennifer A., und Tianxi Cai. „Pathway aggregation for survival prediction via multiple kernel learning“. Statistics in Medicine 37, Nr. 16 (17.04.2018): 2501–15. http://dx.doi.org/10.1002/sim.7681.
Der volle Inhalt der QuelleAzizi, Fityan, und Wahyu Catur Wibowo. „Intermittent Demand Forecasting Using LSTM With Single and Multiple Aggregation“. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, Nr. 5 (02.11.2022): 855–59. http://dx.doi.org/10.29207/resti.v6i5.4435.
Der volle Inhalt der QuelleLiu, Wei, Xiaodong Yue, Yufei Chen und Thierry Denoeux. „Trusted Multi-View Deep Learning with Opinion Aggregation“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 7 (28.06.2022): 7585–93. http://dx.doi.org/10.1609/aaai.v36i7.20724.
Der volle Inhalt der QuelleWang, Zhiqiang, Xinyue Yu, Haoyu Wang und Peiyang Xue. „A federated learning scheme for hierarchical protection and multiple aggregation“. Computers and Electrical Engineering 117 (Juli 2024): 109240. http://dx.doi.org/10.1016/j.compeleceng.2024.109240.
Der volle Inhalt der QuelleLi, Shikun, Shiming Ge, Yingying Hua, Chunhui Zhang, Hao Wen, Tengfei Liu und Weiqiang Wang. „Coupled-View Deep Classifier Learning from Multiple Noisy Annotators“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 04 (03.04.2020): 4667–74. http://dx.doi.org/10.1609/aaai.v34i04.5898.
Der volle Inhalt der QuelleMansouri, Mohamad, Melek Önen, Wafa Ben Jaballah und Mauro Conti. „SoK: Secure Aggregation Based on Cryptographic Schemes for Federated Learning“. Proceedings on Privacy Enhancing Technologies 2023, Nr. 1 (Januar 2023): 140–57. http://dx.doi.org/10.56553/popets-2023-0009.
Der volle Inhalt der QuelleLiu, Chang, Zhuocheng Zou, Yuan Miao und Jun Qiu. „Light field quality assessment based on aggregation learning of multiple visual features“. Optics Express 30, Nr. 21 (30.09.2022): 38298. http://dx.doi.org/10.1364/oe.467754.
Der volle Inhalt der QuellePrice, Stanton R., Derek T. Anderson, Timothy C. Havens und Steven R. Price. „Kernel Matrix-Based Heuristic Multiple Kernel Learning“. Mathematics 10, Nr. 12 (11.06.2022): 2026. http://dx.doi.org/10.3390/math10122026.
Der volle Inhalt der QuelleDissertationen zum Thema "Multiple Aggregation Learning"
Cheung, Chi-Wai. „Probabilistic rank aggregation for multiple SVM ranking /“. View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20CHEUNG.
Der volle Inhalt der QuelleTandon, Prateek. „Bayesian Aggregation of Evidence for Detection and Characterization of Patterns in Multiple Noisy Observations“. Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/658.
Der volle Inhalt der QuelleOrazi, Filippo. „Quantum machine learning: development and evaluation of the Multiple Aggregator Quantum Algorithm“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25062/.
Der volle Inhalt der QuelleMazari, Ahmed. „Apprentissage profond pour la reconnaissance d’actions en vidéos“. Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS171.
Der volle Inhalt der QuelleNowadays, video contents are ubiquitous through the popular use of internet and smartphones, as well as social media. Many daily life applications such as video surveillance and video captioning, as well as scene understanding require sophisticated technologies to process video data. It becomes of crucial importance to develop automatic means to analyze and to interpret the large amount of available video data. In this thesis, we are interested in video action recognition, i.e. the problem of assigning action categories to sequences of videos. This can be seen as a key ingredient to build the next generation of vision systems. It is tackled with AI frameworks, mainly with ML and Deep ConvNets. Current ConvNets are increasingly deeper, data-hungrier and this makes their success tributary of the abundance of labeled training data. ConvNets also rely on (max or average) pooling which reduces dimensionality of output layers (and hence attenuates their sensitivity to the availability of labeled data); however, this process may dilute the information of upstream convolutional layers and thereby affect the discrimination power of the trained video representations, especially when the learned action categories are fine-grained
Jiang, Ju. „A Framework for Aggregation of Multiple Reinforcement Learning Algorithms“. Thesis, 2007. http://hdl.handle.net/10012/2752.
Der volle Inhalt der QuelleBuchteile zum Thema "Multiple Aggregation Learning"
Khan, Muhammad Irfan, Mojtaba Jafaritadi, Esa Alhoniemi, Elina Kontio und Suleiman A. Khan. „Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation“. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 455–69. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09002-8_40.
Der volle Inhalt der QuelleMächler, Leon, Ivan Ezhov, Suprosanna Shit und Johannes C. Paetzold. „FedPIDAvg: A PID Controller Inspired Aggregation Method for Federated Learning“. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 209–17. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44153-0_20.
Der volle Inhalt der QuelleKhan, Muhammad Irfan, Mohammad Ayyaz Azeem, Esa Alhoniemi, Elina Kontio, Suleiman A. Khan und Mojtaba Jafaritadi. „Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation“. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 121–32. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44153-0_12.
Der volle Inhalt der QuelleSingh, Gaurav. „A Local Score Strategy for Weight Aggregation in Federated Learning“. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 133–41. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44153-0_13.
Der volle Inhalt der QuelleWang, Yuan, Renuga Kanagavelu, Qingsong Wei, Yechao Yang und Yong Liu. „Model Aggregation for Federated Learning Considering Non-IID and Imbalanced Data Distribution“. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 196–208. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44153-0_19.
Der volle Inhalt der QuelleSandhofer, Catherine, und Christina Schonberg. „Multiple Examples Support Children’s Word Learning: The Roles of Aggregation, Decontextualization, and Memory Dynamics“. In Language and Concept Acquisition from Infancy Through Childhood, 159–78. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35594-4_8.
Der volle Inhalt der QuelleChen, Yanjia, Ziwang Huang, Hejun Wu und Hao Cai. „Melanoma Classification with IoT Devices from Local and Global Aggregation by Multiple Instance Learning“. In Lecture Notes in Electrical Engineering, 385–91. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0416-7_39.
Der volle Inhalt der QuelleGharahighehi, Alireza, Celine Vens und Konstantinos Pliakos. „Multi-stakeholder News Recommendation Using Hypergraph Learning“. In ECML PKDD 2020 Workshops, 531–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65965-3_36.
Der volle Inhalt der QuelleAlharbi, Ebtisaam, Leandro Soriano Marcolino, Antonios Gouglidis und Qiang Ni. „Robust Federated Learning Method Against Data and Model Poisoning Attacks with Heterogeneous Data Distribution“. In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230257.
Der volle Inhalt der QuelleKhanh, Phan Truong, Tran Thi Hong Ngoc und Sabyasachi Pramanik. „Engineering, Geology, Climate, and Socioeconomic Aspects' Implications on Machine Learning-Dependent Water Pipe Collapse Prediction“. In Methodologies, Frameworks, and Applications of Machine Learning, 161–86. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1062-5.ch009.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Multiple Aggregation Learning"
Jiang, Zoe L., Hui Guo, Yijian Pan, Yang Liu, Xuan Wang und Jun Zhang. „Secure Neural Network in Federated Learning with Model Aggregation under Multiple Keys“. In 2021 8th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2021 7th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2021. http://dx.doi.org/10.1109/cscloud-edgecom52276.2021.00019.
Der volle Inhalt der QuelleYoshida, Takeshi, Kazuki Uehara, Hidenori Sakanashi, Hirokazu Nosato und Masahiro Murakawa. „Multi-Scale Feature Aggregation Based Multiple Instance Learning for Pathological Image Classification“. In 12th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011615200003411.
Der volle Inhalt der QuelleWang, Qianru, Qingyang Li, Bin Guo und Jiangtao Cui. „Efficient Federated Learning with Smooth Aggregation for Non-IID Data from Multiple Edges“. In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10447506.
Der volle Inhalt der QuelleZhang, Jianxin, Cunqiao Hou, Wen Zhu, Mingli Zhang, Ying Zou, Lizhi Zhang und Qiang Zhang. „Attention multiple instance learning with Transformer aggregation for breast cancer whole slide image classification“. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9994848.
Der volle Inhalt der QuelleGarcia Oliveira, Renata, und Wouter Caarls. „Comparing Action Aggregation Strategies in Deep Reinforcement Learning with Continuous Action“. In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1547.
Der volle Inhalt der QuelleWan, Wei, Shengshan Hu, jianrong Lu, Leo Yu Zhang, Hai Jin und Yuanyuan He. „Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection“. 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.2022/106.
Der volle Inhalt der QuelleLi, Wei, Tianzhao Yang, Xiao Wu und Zhaoquan Yuan. „Learning Graph-based Residual Aggregation Network for Group Activity Recognition“. 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.2022/154.
Der volle Inhalt der QuelleGuruprasad, Kamalesh Kumar Mandakolathur, Gayatri Sunil Ambulkar und Geetha Nair. „Federated Learning for Seismic Data Denoising: Privacy-Preserving Paradigm“. In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23888-ms.
Der volle Inhalt der QuelleLi, Zizhuo, Shihua Zhang und Jiayi Ma. „U-Match: Two-view Correspondence Learning with Hierarchy-aware Local Context Aggregation“. In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/130.
Der volle Inhalt der QuelleHan, Zhizhong, Xiyang Wang, Chi Man Vong, Yu-Shen Liu, Matthias Zwicker und C. L. Philip Chen. „3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention“. In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/107.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Multiple Aggregation Learning"
Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe und Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, Dezember 2021. http://dx.doi.org/10.53328/uxuo4751.
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