Gotowa bibliografia na temat „Multiple Aggregation Learning”
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Artykuły w czasopismach na temat "Multiple Aggregation Learning"
JIANG, JU, MOHAMED S. KAMEL i LEI CHEN. "AGGREGATION OF MULTIPLE REINFORCEMENT LEARNING ALGORITHMS". International Journal on Artificial Intelligence Tools 15, nr 05 (październik 2006): 855–61. http://dx.doi.org/10.1142/s0218213006002990.
Pełny tekst źródłaAydin, Bahadir, Yavuz Selim Yilmaz Yavuz Selim Yilmaz, Yaliang Li, Qi Li, Jing Gao i 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.
Pełny tekst źródłaSinnott, Jennifer A., i 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.
Pełny tekst źródłaAzizi, Fityan, i Wahyu Catur Wibowo. "Intermittent Demand Forecasting Using LSTM With Single and Multiple Aggregation". Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, nr 5 (2.11.2022): 855–59. http://dx.doi.org/10.29207/resti.v6i5.4435.
Pełny tekst źródłaLiu, Wei, Xiaodong Yue, Yufei Chen i 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.
Pełny tekst źródłaWang, Zhiqiang, Xinyue Yu, Haoyu Wang i Peiyang Xue. "A federated learning scheme for hierarchical protection and multiple aggregation". Computers and Electrical Engineering 117 (lipiec 2024): 109240. http://dx.doi.org/10.1016/j.compeleceng.2024.109240.
Pełny tekst źródłaLi, Shikun, Shiming Ge, Yingying Hua, Chunhui Zhang, Hao Wen, Tengfei Liu i Weiqiang Wang. "Coupled-View Deep Classifier Learning from Multiple Noisy Annotators". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 04 (3.04.2020): 4667–74. http://dx.doi.org/10.1609/aaai.v34i04.5898.
Pełny tekst źródłaMansouri, Mohamad, Melek Önen, Wafa Ben Jaballah i Mauro Conti. "SoK: Secure Aggregation Based on Cryptographic Schemes for Federated Learning". Proceedings on Privacy Enhancing Technologies 2023, nr 1 (styczeń 2023): 140–57. http://dx.doi.org/10.56553/popets-2023-0009.
Pełny tekst źródłaLiu, Chang, Zhuocheng Zou, Yuan Miao i 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.
Pełny tekst źródłaPrice, Stanton R., Derek T. Anderson, Timothy C. Havens i 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.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaTandon, 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.
Pełny tekst źródłaOrazi, 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/.
Pełny tekst źródłaMazari, Ahmed. "Apprentissage profond pour la reconnaissance d’actions en vidéos". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS171.
Pełny tekst źródłaNowadays, 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.
Pełny tekst źródłaCzęści książek na temat "Multiple Aggregation Learning"
Khan, Muhammad Irfan, Mojtaba Jafaritadi, Esa Alhoniemi, Elina Kontio i Suleiman A. Khan. "Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation". W 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.
Pełny tekst źródłaMächler, Leon, Ivan Ezhov, Suprosanna Shit i Johannes C. Paetzold. "FedPIDAvg: A PID Controller Inspired Aggregation Method for Federated Learning". W 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.
Pełny tekst źródłaKhan, Muhammad Irfan, Mohammad Ayyaz Azeem, Esa Alhoniemi, Elina Kontio, Suleiman A. Khan i Mojtaba Jafaritadi. "Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation". W 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.
Pełny tekst źródłaSingh, Gaurav. "A Local Score Strategy for Weight Aggregation in Federated Learning". W 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.
Pełny tekst źródłaWang, Yuan, Renuga Kanagavelu, Qingsong Wei, Yechao Yang i Yong Liu. "Model Aggregation for Federated Learning Considering Non-IID and Imbalanced Data Distribution". W 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.
Pełny tekst źródłaSandhofer, Catherine, i Christina Schonberg. "Multiple Examples Support Children’s Word Learning: The Roles of Aggregation, Decontextualization, and Memory Dynamics". W 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.
Pełny tekst źródłaChen, Yanjia, Ziwang Huang, Hejun Wu i Hao Cai. "Melanoma Classification with IoT Devices from Local and Global Aggregation by Multiple Instance Learning". W Lecture Notes in Electrical Engineering, 385–91. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0416-7_39.
Pełny tekst źródłaGharahighehi, Alireza, Celine Vens i Konstantinos Pliakos. "Multi-stakeholder News Recommendation Using Hypergraph Learning". W ECML PKDD 2020 Workshops, 531–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65965-3_36.
Pełny tekst źródłaAlharbi, Ebtisaam, Leandro Soriano Marcolino, Antonios Gouglidis i Qiang Ni. "Robust Federated Learning Method Against Data and Model Poisoning Attacks with Heterogeneous Data Distribution". W Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230257.
Pełny tekst źródłaKhanh, Phan Truong, Tran Thi Hong Ngoc i Sabyasachi Pramanik. "Engineering, Geology, Climate, and Socioeconomic Aspects' Implications on Machine Learning-Dependent Water Pipe Collapse Prediction". W Methodologies, Frameworks, and Applications of Machine Learning, 161–86. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1062-5.ch009.
Pełny tekst źródłaStreszczenia konferencji na temat "Multiple Aggregation Learning"
Jiang, Zoe L., Hui Guo, Yijian Pan, Yang Liu, Xuan Wang i Jun Zhang. "Secure Neural Network in Federated Learning with Model Aggregation under Multiple Keys". W 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.
Pełny tekst źródłaYoshida, Takeshi, Kazuki Uehara, Hidenori Sakanashi, Hirokazu Nosato i Masahiro Murakawa. "Multi-Scale Feature Aggregation Based Multiple Instance Learning for Pathological Image Classification". W 12th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011615200003411.
Pełny tekst źródłaWang, Qianru, Qingyang Li, Bin Guo i Jiangtao Cui. "Efficient Federated Learning with Smooth Aggregation for Non-IID Data from Multiple Edges". W ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10447506.
Pełny tekst źródłaZhang, Jianxin, Cunqiao Hou, Wen Zhu, Mingli Zhang, Ying Zou, Lizhi Zhang i Qiang Zhang. "Attention multiple instance learning with Transformer aggregation for breast cancer whole slide image classification". W 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9994848.
Pełny tekst źródłaGarcia Oliveira, Renata, i Wouter Caarls. "Comparing Action Aggregation Strategies in Deep Reinforcement Learning with Continuous Action". W Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1547.
Pełny tekst źródłaWan, Wei, Shengshan Hu, jianrong Lu, Leo Yu Zhang, Hai Jin i Yuanyuan He. "Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection". W 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.
Pełny tekst źródłaLi, Wei, Tianzhao Yang, Xiao Wu i Zhaoquan Yuan. "Learning Graph-based Residual Aggregation Network for Group Activity Recognition". W 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.
Pełny tekst źródłaGuruprasad, Kamalesh Kumar Mandakolathur, Gayatri Sunil Ambulkar i Geetha Nair. "Federated Learning for Seismic Data Denoising: Privacy-Preserving Paradigm". W International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23888-ms.
Pełny tekst źródłaLi, Zizhuo, Shihua Zhang i Jiayi Ma. "U-Match: Two-view Correspondence Learning with Hierarchy-aware Local Context Aggregation". W 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.
Pełny tekst źródłaHan, Zhizhong, Xiyang Wang, Chi Man Vong, Yu-Shen Liu, Matthias Zwicker i C. L. Philip Chen. "3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention". W 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.
Pełny tekst źródłaRaporty organizacyjne na temat "Multiple Aggregation Learning"
Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe i Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, grudzień 2021. http://dx.doi.org/10.53328/uxuo4751.
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