Artykuły w czasopismach na temat „Multiple Aggregation Learning”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Sprawdź 50 najlepszych artykułów w czasopismach naukowych na temat „Multiple Aggregation Learning”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Przeglądaj artykuły w czasopismach z różnych dziedzin i twórz odpowiednie bibliografie.
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 źródłaTam, Prohim, Seungwoo Kang, Seyha Ros i Seokhoon Kim. "Enhancing QoS with LSTM-Based Prediction for Congestion-Aware Aggregation Scheduling in Edge Federated Learning". Electronics 12, nr 17 (27.08.2023): 3615. http://dx.doi.org/10.3390/electronics12173615.
Pełny tekst źródłaBorghei, Benny B., i Thomas Magnusson. "Niche aggregation through cumulative learning: A study of multiple electric bus projects". Environmental Innovation and Societal Transitions 28 (wrzesień 2018): 108–21. http://dx.doi.org/10.1016/j.eist.2018.01.004.
Pełny tekst źródłaCarbonneau, Marc-Andre, Eric Granger i Ghyslain Gagnon. "Bag-Level Aggregation for Multiple-Instance Active Learning in Instance Classification Problems". IEEE Transactions on Neural Networks and Learning Systems 30, nr 5 (maj 2019): 1441–51. http://dx.doi.org/10.1109/tnnls.2018.2869164.
Pełny tekst źródłaLiu, Fei, Zheng Xiong, Wei Yu, Jia Wu, Zheng Kong, Yunhang Ji, Suwei Xu i Mingtao Ji. "Efficient Federated Learning for Feature Aggregation with Heterogenous Edge Devices". Journal of Physics: Conference Series 2665, nr 1 (1.12.2023): 012007. http://dx.doi.org/10.1088/1742-6596/2665/1/012007.
Pełny tekst źródłaReiman, Derek, Ahmed Metwally, Jun Sun i Yang Dai. "Meta-Signer: Metagenomic Signature Identifier based onrank aggregation of features". F1000Research 10 (9.03.2021): 194. http://dx.doi.org/10.12688/f1000research.27384.1.
Pełny tekst źródłaAviv Segev, John Pomerat. "A Comparison of Methods for Neural Network Aggregation". Advances in Artificial Intelligence and Machine Learning 03, nr 02 (2023): 1012–24. http://dx.doi.org/10.54364/aaiml.2023.1160.
Pełny tekst źródłaSo, Jinhyun, Ramy E. Ali, Başak Güler, Jiantao Jiao i A. Salman Avestimehr. "Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 8 (26.06.2023): 9864–73. http://dx.doi.org/10.1609/aaai.v37i8.26177.
Pełny tekst źródłaKaltsounis, Anastasios, Evangelos Spiliotis i Vassilios Assimakopoulos. "Conditional Temporal Aggregation for Time Series Forecasting Using Feature-Based Meta-Learning". Algorithms 16, nr 4 (12.04.2023): 206. http://dx.doi.org/10.3390/a16040206.
Pełny tekst źródłaKim, Sunghun, i Eunjee Lee. "A deep attention LSTM embedded aggregation network for multiple histopathological images". PLOS ONE 18, nr 6 (29.06.2023): e0287301. http://dx.doi.org/10.1371/journal.pone.0287301.
Pełny tekst źródłaFu, Fengjie, Dianhai Wang, Meng Sun, Rui Xie i Zhengyi Cai. "Urban Traffic Flow Prediction Based on Bayesian Deep Learning Considering Optimal Aggregation Time Interval". Sustainability 16, nr 5 (22.02.2024): 1818. http://dx.doi.org/10.3390/su16051818.
Pełny tekst źródłaLi, Weisheng, Maolin He i Minghao Xiang. "Double-Stack Aggregation Network Using a Feature-Travel Strategy for Pansharpening". Remote Sensing 14, nr 17 (27.08.2022): 4224. http://dx.doi.org/10.3390/rs14174224.
Pełny tekst źródłaZhang, Hesheng, Ping Zhang, Mingkai Hu, Muhua Liu i Jiechang Wang. "FedUB: Federated Learning Algorithm Based on Update Bias". Mathematics 12, nr 10 (20.05.2024): 1601. http://dx.doi.org/10.3390/math12101601.
Pełny tekst źródłaLiu, Bowen, i Qiang Tang. "Secure Data Sharing in Federated Learning through Blockchain-Based Aggregation". Future Internet 16, nr 4 (15.04.2024): 133. http://dx.doi.org/10.3390/fi16040133.
Pełny tekst źródłaPapageorgiou, Konstantinos, Pramod K. Singh, Elpiniki Papageorgiou, Harpalsinh Chudasama, Dionysis Bochtis i George Stamoulis. "Fuzzy Cognitive Map-Based Sustainable Socio-Economic Development Planning for Rural Communities". Sustainability 12, nr 1 (30.12.2019): 305. http://dx.doi.org/10.3390/su12010305.
Pełny tekst źródłaWARDELL, DEAN C., i GILBERT L. PETERSON. "FUZZY STATE AGGREGATION AND POLICY HILL CLIMBING FOR STOCHASTIC ENVIRONMENTS". International Journal of Computational Intelligence and Applications 06, nr 03 (wrzesień 2006): 413–28. http://dx.doi.org/10.1142/s1469026806001903.
Pełny tekst źródłaZhang, Chengdong, Keke Li, Shaoqing Wang, Bin Zhou, Lei Wang i Fuzhen Sun. "Learning Heterogeneous Graph Embedding with Metapath-Based Aggregation for Link Prediction". Mathematics 11, nr 3 (21.01.2023): 578. http://dx.doi.org/10.3390/math11030578.
Pełny tekst źródłaNakai, Tsunato, Ye Wang, Kota Yoshida i Takeshi Fujino. "SEDMA: Self-Distillation with Model Aggregation for Membership Privacy". Proceedings on Privacy Enhancing Technologies 2024, nr 1 (styczeń 2024): 494–508. http://dx.doi.org/10.56553/popets-2024-0029.
Pełny tekst źródłaGao, Yilin, i Fengzhu Sun. "Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies". PLOS Computational Biology 19, nr 10 (16.10.2023): e1010608. http://dx.doi.org/10.1371/journal.pcbi.1010608.
Pełny tekst źródłaBonawitz, Kallista, Peter Kairouz, Brendan McMahan i Daniel Ramage. "Federated Learning and Privacy". Queue 19, nr 5 (31.10.2021): 87–114. http://dx.doi.org/10.1145/3494834.3500240.
Pełny tekst źródłaMu, Shengdong, Boyu Liu, Chaolung Lien i Nedjah Nadia. "Optimization of Personal Credit Evaluation Based on a Federated Deep Learning Model". Mathematics 11, nr 21 (31.10.2023): 4499. http://dx.doi.org/10.3390/math11214499.
Pełny tekst źródłaWang, Yabin, Zhiheng Ma, Zhiwu Huang, Yaowei Wang, Zhou Su i Xiaopeng Hong. "Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 8 (26.06.2023): 10209–17. http://dx.doi.org/10.1609/aaai.v37i8.26216.
Pełny tekst źródłaMbonu, Washington Enyinna, Carsten Maple i Gregory Epiphaniou. "An End-Process Blockchain-Based Secure Aggregation Mechanism Using Federated Machine Learning". Electronics 12, nr 21 (5.11.2023): 4543. http://dx.doi.org/10.3390/electronics12214543.
Pełny tekst źródłaPires, Jorge Manuel, i Manuel Pérez Cota. "Metadata as an Aggregation Final Model in Learning Environments". International Journal of Technology Diffusion 7, nr 4 (październik 2016): 36–59. http://dx.doi.org/10.4018/ijtd.2016100103.
Pełny tekst źródłaZhang, Yani, Huailin Zhao, Zuodong Duan, Liangjun Huang, Jiahao Deng i Qing Zhang. "Congested Crowd Counting via Adaptive Multi-Scale Context Learning". Sensors 21, nr 11 (29.05.2021): 3777. http://dx.doi.org/10.3390/s21113777.
Pełny tekst źródłaLu, Yao, Keweiqi Wang i Erbao He. "Many-to-Many Data Aggregation Scheduling Based on Multi-Agent Learning for Multi-Channel WSN". Electronics 11, nr 20 (18.10.2022): 3356. http://dx.doi.org/10.3390/electronics11203356.
Pełny tekst źródłaWang, Rong, i Wei-Tek Tsai. "Asynchronous Federated Learning System Based on Permissioned Blockchains". Sensors 22, nr 4 (21.02.2022): 1672. http://dx.doi.org/10.3390/s22041672.
Pełny tekst źródłaZhou, Chendi, Ji Liu, Juncheng Jia, Jingbo Zhou, Yang Zhou, Huaiyu Dai i Dejing Dou. "Efficient Device Scheduling with Multi-Job Federated Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 9 (28.06.2022): 9971–79. http://dx.doi.org/10.1609/aaai.v36i9.21235.
Pełny tekst źródłaYang, Fangfang, Yanxu Liu, Linlin Xu, Kui Li, Panpan Hu i Jixing Chen. "Vegetation-Ice-Bare Land Cover Conversion in the Oceanic Glacial Region of Tibet Based on Multiple Machine Learning Classifications". Remote Sensing 12, nr 6 (20.03.2020): 999. http://dx.doi.org/10.3390/rs12060999.
Pełny tekst źródłaJin, Xuan, Yuanzhi Yao i Nenghai Yu. "Efficient secure aggregation for privacy-preserving federated learning based on secret sharing". JUSTC 53, nr 4 (2023): 1. http://dx.doi.org/10.52396/justc-2022-0116.
Pełny tekst źródłaSpeck, David, André Biedenkapp, Frank Hutter, Robert Mattmüller i Marius Lindauer. "Learning Heuristic Selection with Dynamic Algorithm Configuration". Proceedings of the International Conference on Automated Planning and Scheduling 31 (17.05.2021): 597–605. http://dx.doi.org/10.1609/icaps.v31i1.16008.
Pełny tekst źródłaLi, Lu, Jiwei Qin i Jintao Luo. "A Blockchain-Based Federated-Learning Framework for Defense against Backdoor Attacks". Electronics 12, nr 11 (1.06.2023): 2500. http://dx.doi.org/10.3390/electronics12112500.
Pełny tekst źródłaMao, Axiu, Endai Huang, Haiming Gan i Kai Liu. "FedAAR: A Novel Federated Learning Framework for Animal Activity Recognition with Wearable Sensors". Animals 12, nr 16 (21.08.2022): 2142. http://dx.doi.org/10.3390/ani12162142.
Pełny tekst źródłaLiu, Tong, Akash Venkatachalam, Pratik Sanjay Bongale i Christopher M. Homan. "Learning to Predict Population-Level Label Distributions". Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 7 (28.10.2019): 68–76. http://dx.doi.org/10.1609/hcomp.v7i1.5286.
Pełny tekst źródłaLi, Qingtie, Xuemei Wang i Shougang Ren. "A Privacy Robust Aggregation Method Based on Federated Learning in the IoT". Electronics 12, nr 13 (5.07.2023): 2951. http://dx.doi.org/10.3390/electronics12132951.
Pełny tekst źródłaWu, Xia, Lei Xu i Liehuang Zhu. "Local Differential Privacy-Based Federated Learning under Personalized Settings". Applied Sciences 13, nr 7 (24.03.2023): 4168. http://dx.doi.org/10.3390/app13074168.
Pełny tekst źródłaWang, Mengdi, Anna Bodonhelyi, Efe Bozkir i Enkelejda Kasneci. "TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 14 (24.03.2024): 15546–54. http://dx.doi.org/10.1609/aaai.v38i14.29481.
Pełny tekst źródłaPeng, Cheng, Ke Chen, Lidan Shou i Gang Chen. "CARAT: Contrastive Feature Reconstruction and Aggregation for Multi-Modal Multi-Label Emotion Recognition". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 13 (24.03.2024): 14581–89. http://dx.doi.org/10.1609/aaai.v38i13.29374.
Pełny tekst źródłaDjebrouni, Yasmine, Nawel Benarba, Ousmane Touat, Pasquale De Rosa, Sara Bouchenak, Angela Bonifati, Pascal Felber, Vania Marangozova i Valerio Schiavoni. "Bias Mitigation in Federated Learning for Edge Computing". Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, nr 4 (19.12.2023): 1–35. http://dx.doi.org/10.1145/3631455.
Pełny tekst źródłaFallah, Mahdi, Parya Mohammadi, Mohammadreza NasiriFard i Pedram Salehpour. "Optimizing QoS Metrics for Software-Defined Networking in Federated Learning". Mobile Information Systems 2023 (9.10.2023): 1–10. http://dx.doi.org/10.1155/2023/3896267.
Pełny tekst źródłaWang, Shuohang, Yunshi Lan, Yi Tay, Jing Jiang i Jingjing Liu. "Multi-Level Head-Wise Match and Aggregation in Transformer for Textual Sequence Matching". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 05 (3.04.2020): 9209–16. http://dx.doi.org/10.1609/aaai.v34i05.6458.
Pełny tekst źródła