Artigos de revistas sobre o tema "Self-supervised learning (artificial intelligence)"
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Neghawi, Elie, e Yan Liu. "Enhancing Self-Supervised Learning through Explainable Artificial Intelligence Mechanisms: A Computational Analysis". Big Data and Cognitive Computing 8, n.º 6 (3 de junho de 2024): 58. http://dx.doi.org/10.3390/bdcc8060058.
Texto completo da fonteCHAN, JASON, IRENA KOPRINSKA e JOSIAH POON. "SEMI-SUPERVISED CLASSIFICATION USING BRIDGING". International Journal on Artificial Intelligence Tools 17, n.º 03 (junho de 2008): 415–31. http://dx.doi.org/10.1142/s0218213008003972.
Texto completo da fonteYuya, KOBAYASHI, Masahiro SUZUKI e Yutaka MATSUO. "Scene Interpretation Method using Transformer and Self-supervised Learning". Transactions of the Japanese Society for Artificial Intelligence 37, n.º 2 (1 de março de 2022): I—L75_1–17. http://dx.doi.org/10.1527/tjsai.37-2_i-l75.
Texto completo da fonteHrycej, Tomas. "Supporting supervised learning by self-organization". Neurocomputing 4, n.º 1-2 (fevereiro de 1992): 17–30. http://dx.doi.org/10.1016/0925-2312(92)90040-v.
Texto completo da fonteWang, Fei, e Changshui Zhang. "Robust self-tuning semi-supervised learning". Neurocomputing 70, n.º 16-18 (outubro de 2007): 2931–39. http://dx.doi.org/10.1016/j.neucom.2006.11.004.
Texto completo da fonteBiscione, Valerio, e Jeffrey S. Bowers. "Learning online visual invariances for novel objects via supervised and self-supervised training". Neural Networks 150 (junho de 2022): 222–36. http://dx.doi.org/10.1016/j.neunet.2022.02.017.
Texto completo da fonteMa, Jun, Yakun Wen e Liming Yang. "Lagrangian supervised and semi-supervised extreme learning machine". Applied Intelligence 49, n.º 2 (25 de agosto de 2018): 303–18. http://dx.doi.org/10.1007/s10489-018-1273-4.
Texto completo da fonteChe, Feihu, Guohua Yang, Dawei Zhang, Jianhua Tao e Tong Liu. "Self-supervised graph representation learning via bootstrapping". Neurocomputing 456 (outubro de 2021): 88–96. http://dx.doi.org/10.1016/j.neucom.2021.03.123.
Texto completo da fonteGu, Nannan, Pengying Fan, Mingyu Fan e Di Wang. "Structure regularized self-paced learning for robust semi-supervised pattern classification". Neural Computing and Applications 31, n.º 10 (19 de abril de 2018): 6559–74. http://dx.doi.org/10.1007/s00521-018-3478-1.
Texto completo da fonteSaravana Kumar, N. M. "IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN IMPARTING EDUCATION AND EVALUATING STUDENT PERFORMANCE". Journal of Artificial Intelligence and Capsule Networks 01, n.º 01 (2 de setembro de 2019): 1–9. http://dx.doi.org/10.36548/jaicn.2019.1.001.
Texto completo da fonteWei, Chen, Yiping Tang, Chuang Niu Chuang Niu, Haihong Hu, Yue Wang e Jimin Liang. "Self-Supervised Representation Learning for Evolutionary Neural Architecture Search". IEEE Computational Intelligence Magazine 16, n.º 3 (agosto de 2021): 33–49. http://dx.doi.org/10.1109/mci.2021.3084415.
Texto completo da fonteXi, Liang, Zichao Yun, Han Liu, Ruidong Wang, Xunhua Huang e Haoyi Fan. "Semi-supervised Time Series Classification Model with Self-supervised Learning". Engineering Applications of Artificial Intelligence 116 (novembro de 2022): 105331. http://dx.doi.org/10.1016/j.engappai.2022.105331.
Texto completo da fonteSerey, Joel, Luis Quezada, Miguel Alfaro, Guillermo Fuertes, Manuel Vargas, Rodrigo Ternero, Jorge Sabattin, Claudia Duran e Sebastian Gutierrez. "Artificial Intelligence Methodologies for Data Management". Symmetry 13, n.º 11 (29 de outubro de 2021): 2040. http://dx.doi.org/10.3390/sym13112040.
Texto completo da fonteKozhuharov, Mihail. "Artificial Intelligence: Basic Concepts". Педагогически форум 11, n.º 4 (2023): 3–24. http://dx.doi.org/10.15547/pf.2023.023.
Texto completo da fonteTakama, Yasufumi. "Web Intelligence and Artificial Intelligence". Journal of Advanced Computational Intelligence and Intelligent Informatics 21, n.º 1 (20 de janeiro de 2017): 25–30. http://dx.doi.org/10.20965/jaciii.2017.p0025.
Texto completo da fonteLedziński, Łukasz, e Grzegorz Grześk. "Artificial Intelligence Technologies in Cardiology". Journal of Cardiovascular Development and Disease 10, n.º 5 (6 de maio de 2023): 202. http://dx.doi.org/10.3390/jcdd10050202.
Texto completo da fonteYamauchi, K., M. Oota e N. Ishii. "A self-supervised learning system for pattern recognition by sensory integration". Neural Networks 12, n.º 10 (dezembro de 1999): 1347–58. http://dx.doi.org/10.1016/s0893-6080(99)00064-7.
Texto completo da fonteDushkin, R. V. "Semantic Supervised Training for General Artificial Cognitive Agents". Siberian Journal of Philosophy 19, n.º 2 (21 de outubro de 2021): 51–64. http://dx.doi.org/10.25205/2541-7517-2021-19-2-51-64.
Texto completo da fonteFlorence, Peter, Lucas Manuelli e Russ Tedrake. "Self-Supervised Correspondence in Visuomotor Policy Learning". IEEE Robotics and Automation Letters 5, n.º 2 (abril de 2020): 492–99. http://dx.doi.org/10.1109/lra.2019.2956365.
Texto completo da fontePal, S. K., A. Pathak e C. Basu. "Dynamic guard zone for self-supervised learning". Pattern Recognition Letters 7, n.º 3 (março de 1988): 135–44. http://dx.doi.org/10.1016/0167-8655(88)90056-6.
Texto completo da fonteSoni, Kuldeep, Nidhi Pateria e Gulafsha Anjum. "Artificial Intelligence and Machine Learning in Sport Medicines". International Journal of Innovative Research in Computer and Communication Engineering 12, Special Is (25 de março de 2024): 69–73. http://dx.doi.org/10.15680/ijircce.2024.1203511.
Texto completo da fonteLi, Li, Kaiyi Zhao, Sicong Li, Ruizhi Sun e Saihua Cai. "Extreme Learning Machine for Supervised Classification with Self-paced Learning". Neural Processing Letters 52, n.º 3 (14 de junho de 2020): 1723–44. http://dx.doi.org/10.1007/s11063-020-10286-9.
Texto completo da fonteMiranda, Enrique, e Jordi Suñé. "Memristors for Neuromorphic Circuits and Artificial Intelligence Applications". Materials 13, n.º 4 (20 de fevereiro de 2020): 938. http://dx.doi.org/10.3390/ma13040938.
Texto completo da fonteMody, Rohit, Debabrata Dash e Deepanshu Mody. "Artificial intelligence in coronary physiology: where do we stand?" Journal of Transcatheter Interventions 30 (28 de outubro de 2022): 1–9. http://dx.doi.org/10.31160/jotci202230a20220009.
Texto completo da fonteOkadome, Yuya, Kenshiro Ata, Hiroshi Ishiguro e Yutaka Nakamura. "Self-supervised Learning Method for Behavior Prediction during Dialogue Based on Temporal Consistency". Transactions of the Japanese Society for Artificial Intelligence 37, n.º 6 (1 de novembro de 2022): B—M43_1–13. http://dx.doi.org/10.1527/tjsai.37-6_b-m43.
Texto completo da fonteBenavides-Prado, Diana, Yun Sing Koh e Patricia Riddle. "Towards Knowledgeable Supervised Lifelong Learning Systems". Journal of Artificial Intelligence Research 68 (8 de maio de 2020): 159–224. http://dx.doi.org/10.1613/jair.1.11432.
Texto completo da fonteOkori, Washington, e Joseph Obua. "SUPERVISED LEARNING ALGORITHMS FOR FAMINE PREDICTION". Applied Artificial Intelligence 25, n.º 9 (outubro de 2011): 822–35. http://dx.doi.org/10.1080/08839514.2011.611930.
Texto completo da fontePoulos, Jason, e Rafael Valle. "Missing Data Imputation for Supervised Learning". Applied Artificial Intelligence 32, n.º 2 (19 de março de 2018): 186–96. http://dx.doi.org/10.1080/08839514.2018.1448143.
Texto completo da fonteWeinlichová, Jana, e Jiří Fejfar. "Usage of self-organizing neural networks in evaluation of consumer behaviour". Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, n.º 6 (2010): 625–32. http://dx.doi.org/10.11118/actaun201058060625.
Texto completo da fonteHashimoto, Daniel A., Elan Witkowski, Lei Gao, Ozanan Meireles e Guy Rosman. "Artificial Intelligence in Anesthesiology". Anesthesiology 132, n.º 2 (1 de fevereiro de 2020): 379–94. http://dx.doi.org/10.1097/aln.0000000000002960.
Texto completo da fonteLu, Keyu, Chengyi Zeng e Yonghu Zeng. "Self-supervised learning of monocular depth using quantized networks". Neurocomputing 488 (junho de 2022): 634–46. http://dx.doi.org/10.1016/j.neucom.2021.11.071.
Texto completo da fonteHou, Wenjie, Zheyun Qin, Xiaoming Xi, Xiankai Lu e Yilong Yin. "Learning disentangled representation for self-supervised video object segmentation". Neurocomputing 481 (abril de 2022): 270–80. http://dx.doi.org/10.1016/j.neucom.2022.01.066.
Texto completo da fonteChen, Long, Wen Tang, Tao Ruan Wan e Nigel W. John. "Self-supervised monocular image depth learning and confidence estimation". Neurocomputing 381 (março de 2020): 272–81. http://dx.doi.org/10.1016/j.neucom.2019.11.038.
Texto completo da fonteAryal, Gopi. "Artificial intelligence in surgical pathology". Journal of Pathology of Nepal 9, n.º 1 (2 de abril de 2019): I. http://dx.doi.org/10.3126/jpn.v9i1.23444.
Texto completo da fonteXu, Rongge, Ruiyang Hao e Biqing Huang. "Efficient surface defect detection using self-supervised learning strategy and segmentation network". Advanced Engineering Informatics 52 (abril de 2022): 101566. http://dx.doi.org/10.1016/j.aei.2022.101566.
Texto completo da fonteLiu, Chicheng, Libin Song, Jiwen Zhang, Ken Chen e Jing Xu. "Self-Supervised Learning for Specified Latent Representation". IEEE Transactions on Fuzzy Systems 28, n.º 1 (janeiro de 2020): 47–59. http://dx.doi.org/10.1109/tfuzz.2019.2904237.
Texto completo da fontePETROVIC, SMILJANA, e SUSAN L. EPSTEIN. "RANDOM SUBSETS SUPPORT LEARNING A MIXTURE OF HEURISTICS". International Journal on Artificial Intelligence Tools 17, n.º 03 (junho de 2008): 501–20. http://dx.doi.org/10.1142/s0218213008004023.
Texto completo da fonteXu, Ke, Guoqiang Zhong, Zhaoyang Deng, Kang Zhang e Kaizhu Huang. "Self-supervised generative learning for sequential data prediction". Applied Intelligence, 20 de abril de 2023. http://dx.doi.org/10.1007/s10489-023-04578-5.
Texto completo da fonte"Artificial Intelligence Methodologies for Supervised Learning". International Journal of Advanced Research in Big Data Management System 3, n.º 1 (30 de maio de 2019). http://dx.doi.org/10.21742/ijarbms.2019.3.1.03.
Texto completo da fonteLi, Simou, Yuxing Mao, Jian Li, Yihang Xu, Jinsen Li, Xueshuo Chen, Siyang Liu e Xianping Zhao. "FedUTN: federated self-supervised learning with updating target network". Applied Intelligence, 26 de agosto de 2022. http://dx.doi.org/10.1007/s10489-022-04070-6.
Texto completo da fonteKim, Sangwon, Jimi Lee e Byoung Chul Ko. "SSL-MOT: self-supervised learning based multi-object tracking". Applied Intelligence, 22 de abril de 2022. http://dx.doi.org/10.1007/s10489-022-03473-9.
Texto completo da fonteWang, Zhipeng, Chunping Hou, Guanghui Yue e Qingyuan Yang. "Dynamic-boosting attention for self-supervised video representation learning". Applied Intelligence, 1 de julho de 2021. http://dx.doi.org/10.1007/s10489-021-02440-0.
Texto completo da fonteHafez, Muhammad Burhan, e Stefan Wermter. "Continual Robot Learning Using Self-Supervised Task Inference". IEEE Transactions on Cognitive and Developmental Systems, 2023, 1. http://dx.doi.org/10.1109/tcds.2023.3315513.
Texto completo da fonteWang, Jing, Jun Wu, Caiyan Jia e Zhifei Zhang. "Self-supervised variational autoencoder towards recommendation by nested contrastive learning". Applied Intelligence, 14 de fevereiro de 2023. http://dx.doi.org/10.1007/s10489-023-04488-6.
Texto completo da fonteLi, Jinlong, Zequn Jie, Xu Wang, Yu Zhou, Lin Ma e Jianmin Jiang. "Weakly supervised semantic segmentation via self-supervised destruction learning". Neurocomputing, setembro de 2023, 126821. http://dx.doi.org/10.1016/j.neucom.2023.126821.
Texto completo da fonteLiu, Jiabin, Biao Li, Minglong Lei e Yong Shi. "Self-supervised knowledge distillation for complementary label learning". Neural Networks, agosto de 2022. http://dx.doi.org/10.1016/j.neunet.2022.08.014.
Texto completo da fonteHuang, Lang, Chao Zhang e Hongyang Zhang. "Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning". IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 1–17. http://dx.doi.org/10.1109/tpami.2022.3217792.
Texto completo da fonteRafiei, Mohammad H., Lynne V. Gauthier, Hojjat Adeli e Daniel Takabi. "Self-Supervised Learning for Electroencephalography". IEEE Transactions on Neural Networks and Learning Systems, 2022, 1–15. http://dx.doi.org/10.1109/tnnls.2022.3190448.
Texto completo da fonteYe, Fei, e Adrian G. Bors. "Self-supervised adversarial variational learning". Pattern Recognition, novembro de 2023, 110156. http://dx.doi.org/10.1016/j.patcog.2023.110156.
Texto completo da fonteVerleysen, Andreas, Matthijs Biondina e Francis wyffels. "Learning self-supervised task progression metrics: a case of cloth folding". Applied Intelligence, 2 de maio de 2022. http://dx.doi.org/10.1007/s10489-022-03466-8.
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