Artigos de revistas sobre o tema "Representation learning (artifical intelligence)"
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Hamilton, William L. "Graph Representation Learning". Synthesis Lectures on Artificial Intelligence and Machine Learning 14, n.º 3 (15 de setembro de 2020): 1–159. http://dx.doi.org/10.2200/s01045ed1v01y202009aim046.
Texto completo da fonteKonidaris, George, Leslie Pack Kaelbling e Tomas Lozano-Perez. "From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning". Journal of Artificial Intelligence Research 61 (31 de janeiro de 2018): 215–89. http://dx.doi.org/10.1613/jair.5575.
Texto completo da fonteRezayi, Saed. "Learning Better Representations Using Auxiliary Knowledge". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junho de 2023): 16133–34. http://dx.doi.org/10.1609/aaai.v37i13.26927.
Texto completo da fonteFROMMBERGER, LUTZ. "LEARNING TO BEHAVE IN SPACE: A QUALITATIVE SPATIAL REPRESENTATION FOR ROBOT NAVIGATION WITH REINFORCEMENT LEARNING". International Journal on Artificial Intelligence Tools 17, n.º 03 (junho de 2008): 465–82. http://dx.doi.org/10.1142/s021821300800400x.
Texto completo da fonteHaghir Chehreghani, Morteza, e Mostafa Haghir Chehreghani. "Learning representations from dendrograms". Machine Learning 109, n.º 9-10 (16 de agosto de 2020): 1779–802. http://dx.doi.org/10.1007/s10994-020-05895-3.
Texto completo da fonteSaitta, Lorenza. "Representation change in machine learning". AI Communications 9, n.º 1 (1996): 14–20. http://dx.doi.org/10.3233/aic-1996-9102.
Texto completo da fonteRives, Alexander, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo et al. "Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences". Proceedings of the National Academy of Sciences 118, n.º 15 (5 de abril de 2021): e2016239118. http://dx.doi.org/10.1073/pnas.2016239118.
Texto completo da fonteKang, Zhao, Xiao Lu, Jian Liang, Kun Bai e Zenglin Xu. "Relation-Guided Representation Learning". Neural Networks 131 (novembro de 2020): 93–102. http://dx.doi.org/10.1016/j.neunet.2020.07.014.
Texto completo da fonteProrok, Máté. "Applications of artificial intelligence systems". Deliberationes 15, Különszám (2022): 76–88. http://dx.doi.org/10.54230/delib.2022.k.sz.76.
Texto completo da fonteMazoure, Bogdan, Thang Doan, Tianyu Li, Vladimir Makarenkov, Joelle Pineau, Doina Precup e Guillaume Rabusseau. "Low-Rank Representation of Reinforcement Learning Policies". Journal of Artificial Intelligence Research 75 (27 de outubro de 2022): 597–636. http://dx.doi.org/10.1613/jair.1.13854.
Texto completo da fonteLawler, Robert W. "Getting Intelligence Into the Minds of People". LEARNing Landscapes 6, n.º 2 (2 de junho de 2013): 223–47. http://dx.doi.org/10.36510/learnland.v6i2.614.
Texto completo da fonteKoohzadi, Maryam, Nasrollah Moghadam Charkari e Foad Ghaderi. "Unsupervised representation learning based on the deep multi-view ensemble learning". Applied Intelligence 50, n.º 2 (31 de julho de 2019): 562–81. http://dx.doi.org/10.1007/s10489-019-01526-0.
Texto completo da fonteHanson, Stephen José, e David J. Burr. "What connectionist models learn: Learning and representation in connectionist networks". Behavioral and Brain Sciences 13, n.º 3 (setembro de 1990): 471–89. http://dx.doi.org/10.1017/s0140525x00079760.
Texto completo da fonteZheng, Tingyi, Huibin Ge, Jiayi Li e Li Wang. "Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis". Applied Intelligence 51, n.º 1 (10 de agosto de 2020): 248–64. http://dx.doi.org/10.1007/s10489-020-01821-1.
Texto completo da fonteLi, Bentian, e Dechang Pi. "Network representation learning: a systematic literature review". Neural Computing and Applications 32, n.º 21 (20 de abril de 2020): 16647–79. http://dx.doi.org/10.1007/s00521-020-04908-5.
Texto completo da fonteHuang, Ming, Fuzhen Zhuang, Xiao Zhang, Xiang Ao, Zhengyu Niu, Min-Ling Zhang e Qing He. "Supervised representation learning for multi-label classification". Machine Learning 108, n.º 5 (13 de fevereiro de 2019): 747–63. http://dx.doi.org/10.1007/s10994-019-05783-5.
Texto completo da fonteHaghir Chehreghani, Morteza. "Unsupervised representation learning with Minimax distance measures". Machine Learning 109, n.º 11 (28 de julho de 2020): 2063–97. http://dx.doi.org/10.1007/s10994-020-05886-4.
Texto completo da fonteMiyamoto, Hiroyuki, Jun Morimoto, Kenji Doya e Mitsuo Kawato. "Reinforcement learning with via-point representation". Neural Networks 17, n.º 3 (abril de 2004): 299–305. http://dx.doi.org/10.1016/j.neunet.2003.11.004.
Texto completo da fonteTavanaei, Amirhossein, Timothée Masquelier e Anthony Maida. "Representation learning using event-based STDP". Neural Networks 105 (setembro de 2018): 294–303. http://dx.doi.org/10.1016/j.neunet.2018.05.018.
Texto completo da fonteJiao, Pengfei, Hongjiang Chen, Huijun Tang, Qing Bao, Long Zhang, Zhidong Zhao e Huaming Wu. "Contrastive representation learning on dynamic networks". Neural Networks 174 (junho de 2024): 106240. http://dx.doi.org/10.1016/j.neunet.2024.106240.
Texto completo da fonteChikwendu, Ijeoma Amuche, Xiaoling Zhang, Isaac Osei Agyemang, Isaac Adjei-Mensah, Ukwuoma Chiagoziem Chima e Chukwuebuka Joseph Ejiyi. "A Comprehensive Survey on Deep Graph Representation Learning Methods". Journal of Artificial Intelligence Research 78 (25 de outubro de 2023): 287–356. http://dx.doi.org/10.1613/jair.1.14768.
Texto completo da fonteJurewicz, Mateusz, e Leon Derczynski. "Set-to-Sequence Methods in Machine Learning: A Review". Journal of Artificial Intelligence Research 71 (12 de agosto de 2021): 885–924. http://dx.doi.org/10.1613/jair.1.12839.
Texto completo da fonteTadepalli, P., e B. K. Natarajan. "A Formal Framework for Speedup Learning from Problems and Solutions". Journal of Artificial Intelligence Research 4 (1 de junho de 1996): 445–75. http://dx.doi.org/10.1613/jair.154.
Texto completo da fonteQin, Jisheng, Xiaoqin Zeng, Shengli Wu e Yang Zou. "Context-sensitive graph representation learning". Connection Science 34, n.º 1 (14 de setembro de 2022): 2313–31. http://dx.doi.org/10.1080/09540091.2022.2115010.
Texto completo da fonteAshley, Kevin D., e Edwina L. Rissland. "Law, learning and representation". Artificial Intelligence 150, n.º 1-2 (novembro de 2003): 17–58. http://dx.doi.org/10.1016/s0004-3702(03)00109-7.
Texto completo da fonteAL-Fayyadh, Hayder Rahm Dakheel, Salam Abdulabbas Ganim Ali e Dr Basim Abood. "Modelling an Adaptive Learning System Using Artificial Intelligence". Webology 19, n.º 1 (24 de dezembro de 2021): 01–18. http://dx.doi.org/10.14704/web/v19i1/web19001.
Texto completo da fonteMaher, Mary Lou, e Heng Li. "Learning design concepts using machine learning techniques". Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8, n.º 2 (1994): 95–111. http://dx.doi.org/10.1017/s0890060400000706.
Texto completo da fonteHambadjawa, Johan Agung Pramono, e Khaerunnisa. "Development Concept of Artificial Intelligence as an Architect’s Representation: Literature Review". Arsir 8, n.º 1 (22 de março de 2024): 14–25. http://dx.doi.org/10.32502/arsir.v8i1.53.
Texto completo da fonteWang, Meng-Xiang, Wang-Chien Lee, Tao-Yang Fu e Ge Yu. "On Representation Learning for Road Networks". ACM Transactions on Intelligent Systems and Technology 12, n.º 1 (22 de dezembro de 2020): 1–27. http://dx.doi.org/10.1145/3424346.
Texto completo da fonteLu, Run-kun, Jian-wei Liu, Si-ming Lian e Xin Zuo. "Multi-view representation learning in multi-task scene". Neural Computing and Applications 32, n.º 14 (29 de outubro de 2019): 10403–22. http://dx.doi.org/10.1007/s00521-019-04577-z.
Texto completo da fonteXie, Ruobing, Stefan Heinrich, Zhiyuan Liu, Cornelius Weber, Yuan Yao, Stefan Wermter e Maosong Sun. "Integrating Image-Based and Knowledge-Based Representation Learning". IEEE Transactions on Cognitive and Developmental Systems 12, n.º 2 (junho de 2020): 169–78. http://dx.doi.org/10.1109/tcds.2019.2906685.
Texto completo da fonteSun, Yanan, Hua Mao, Yongsheng Sang e Zhang Yi. "Explicit guiding auto-encoders for learning meaningful representation". Neural Computing and Applications 28, n.º 3 (20 de outubro de 2015): 429–36. http://dx.doi.org/10.1007/s00521-015-2082-x.
Texto completo da fonteDietterich, T. G. "Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition". Journal of Artificial Intelligence Research 13 (1 de novembro de 2000): 227–303. http://dx.doi.org/10.1613/jair.639.
Texto completo da fonteKocabas, S. "A review of learning". Knowledge Engineering Review 6, n.º 3 (setembro de 1991): 195–222. http://dx.doi.org/10.1017/s0269888900005804.
Texto completo da fonteO’Mahony, Niall, Sean Campbell, Lenka Krpalkova, Anderson Carvalho, Joseph Walsh e Daniel Riordan. "Representation Learning for Fine-Grained Change Detection". Sensors 21, n.º 13 (30 de junho de 2021): 4486. http://dx.doi.org/10.3390/s21134486.
Texto completo da fonteLesort, Timothée, Natalia Díaz-Rodríguez, Jean-Frano̧is Goudou e David Filliat. "State representation learning for control: An overview". Neural Networks 108 (dezembro de 2018): 379–92. http://dx.doi.org/10.1016/j.neunet.2018.07.006.
Texto completo da fonteFRANKLIN, JUDY A., e KRYSTAL K. LOCKE. "RECURRENT NEURAL NETWORKS FOR MUSICAL PITCH MEMORY AND CLASSIFICATION". International Journal on Artificial Intelligence Tools 14, n.º 01n02 (fevereiro de 2005): 329–42. http://dx.doi.org/10.1142/s0218213005002120.
Texto completo da fonteShui, Changjian, Boyu Wang e Christian Gagné. "On the benefits of representation regularization in invariance based domain generalization". Machine Learning 111, n.º 3 (1 de janeiro de 2022): 895–915. http://dx.doi.org/10.1007/s10994-021-06080-w.
Texto completo da fonteLi, Fuzhen, Zhenfeng Zhu, Xingxing Zhang, Jian Cheng e Yao Zhao. "Diffusion induced graph representation learning". Neurocomputing 360 (setembro de 2019): 220–29. http://dx.doi.org/10.1016/j.neucom.2019.06.012.
Texto completo da fonteLittman, David, e Maarten van Someren. "International Workshop on Knowledge Representation and Organization in Machine Learning". AI Communications 1, n.º 1 (1988): 44–45. http://dx.doi.org/10.3233/aic-1988-1108.
Texto completo da fonteZeng, Deyu, Jing Sun, Zongze Wu, Chris Ding e Zhigang Ren. "Data representation learning via dictionary learning and self-representation". Applied Intelligence, 31 de agosto de 2023. http://dx.doi.org/10.1007/s10489-023-04902-z.
Texto completo da fonteMerckling, Astrid, Nicolas Perrin-Gilbert, Alex Coninx e Stéphane Doncieux. "Exploratory State Representation Learning". Frontiers in Robotics and AI 9 (14 de fevereiro de 2022). http://dx.doi.org/10.3389/frobt.2022.762051.
Texto completo da fonteDeshmukh, Aniket Anand, Jayanth Reddy Regatti, Eren Manavoglu e Urun Dogan. "Representation learning for clustering via building consensus". Machine Learning, 9 de setembro de 2022. http://dx.doi.org/10.1007/s10994-022-06194-9.
Texto completo da fonteXu, Lingling, Haoran Xie, Zongxi Li, Fu Lee Wang, Weiming Wang e Qing Li. "Contrastive Learning Models for Sentence Representations". ACM Transactions on Intelligent Systems and Technology, 2 de maio de 2023. http://dx.doi.org/10.1145/3593590.
Texto completo da fonteWang, Yuwei, e Yi Zeng. "Statistical Analysis of Multisensory and Text-Derived Representations on Concept Learning". Frontiers in Computational Neuroscience 16 (27 de abril de 2022). http://dx.doi.org/10.3389/fncom.2022.861265.
Texto completo da fonteWickstrøm, Kristoffer K., Daniel J. Trosten, Sigurd Løkse, Ahcène Boubekki, Karl øyvind Mikalsen, Michael C. Kampffmeyer e Robert Jenssen. "RELAX: Representation Learning Explainability". International Journal of Computer Vision, 11 de março de 2023. http://dx.doi.org/10.1007/s11263-023-01773-2.
Texto completo da fonteHiggins, Irina, Sébastien Racanière e Danilo Rezende. "Symmetry-Based Representations for Artificial and Biological General Intelligence". Frontiers in Computational Neuroscience 16 (14 de abril de 2022). http://dx.doi.org/10.3389/fncom.2022.836498.
Texto completo da fonteJeub, Lucas G. S., Giovanni Colavizza, Xiaowen Dong, Marya Bazzi e Mihai Cucuringu. "Local2Global: a distributed approach for scaling representation learning on graphs". Machine Learning, 24 de fevereiro de 2023. http://dx.doi.org/10.1007/s10994-022-06285-7.
Texto completo da fonteOuyang, Tinghui, e Xun Shen. "Representation learning based on hybrid polynomial approximated extreme learning machine". Applied Intelligence, 26 de outubro de 2021. http://dx.doi.org/10.1007/s10489-021-02915-0.
Texto completo da fonteBorrego-Díaz, Joaquín, e Juan Galán Páez. "Knowledge representation for explainable artificial intelligence". Complex & Intelligent Systems, 4 de janeiro de 2022. http://dx.doi.org/10.1007/s40747-021-00613-5.
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