Artículos de revistas sobre el 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 septiembre de 2020): 1–159. http://dx.doi.org/10.2200/s01045ed1v01y202009aim046.
Texto completoKonidaris, George, Leslie Pack Kaelbling y Tomas Lozano-Perez. "From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning". Journal of Artificial Intelligence Research 61 (31 de enero de 2018): 215–89. http://dx.doi.org/10.1613/jair.5575.
Texto completoRezayi, Saed. "Learning Better Representations Using Auxiliary Knowledge". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junio de 2023): 16133–34. http://dx.doi.org/10.1609/aaai.v37i13.26927.
Texto completoFROMMBERGER, 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 (junio de 2008): 465–82. http://dx.doi.org/10.1142/s021821300800400x.
Texto completoHaghir Chehreghani, Morteza y 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 completoSaitta, 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 completoRives, 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 completoKang, Zhao, Xiao Lu, Jian Liang, Kun Bai y Zenglin Xu. "Relation-Guided Representation Learning". Neural Networks 131 (noviembre de 2020): 93–102. http://dx.doi.org/10.1016/j.neunet.2020.07.014.
Texto completoProrok, Máté. "Applications of artificial intelligence systems". Deliberationes 15, Különszám (2022): 76–88. http://dx.doi.org/10.54230/delib.2022.k.sz.76.
Texto completoMazoure, Bogdan, Thang Doan, Tianyu Li, Vladimir Makarenkov, Joelle Pineau, Doina Precup y Guillaume Rabusseau. "Low-Rank Representation of Reinforcement Learning Policies". Journal of Artificial Intelligence Research 75 (27 de octubre de 2022): 597–636. http://dx.doi.org/10.1613/jair.1.13854.
Texto completoLawler, Robert W. "Getting Intelligence Into the Minds of People". LEARNing Landscapes 6, n.º 2 (2 de junio de 2013): 223–47. http://dx.doi.org/10.36510/learnland.v6i2.614.
Texto completoKoohzadi, Maryam, Nasrollah Moghadam Charkari y Foad Ghaderi. "Unsupervised representation learning based on the deep multi-view ensemble learning". Applied Intelligence 50, n.º 2 (31 de julio de 2019): 562–81. http://dx.doi.org/10.1007/s10489-019-01526-0.
Texto completoHanson, Stephen José y David J. Burr. "What connectionist models learn: Learning and representation in connectionist networks". Behavioral and Brain Sciences 13, n.º 3 (septiembre de 1990): 471–89. http://dx.doi.org/10.1017/s0140525x00079760.
Texto completoZheng, Tingyi, Huibin Ge, Jiayi Li y 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 completoLi, Bentian y 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 completoHuang, Ming, Fuzhen Zhuang, Xiao Zhang, Xiang Ao, Zhengyu Niu, Min-Ling Zhang y Qing He. "Supervised representation learning for multi-label classification". Machine Learning 108, n.º 5 (13 de febrero de 2019): 747–63. http://dx.doi.org/10.1007/s10994-019-05783-5.
Texto completoHaghir Chehreghani, Morteza. "Unsupervised representation learning with Minimax distance measures". Machine Learning 109, n.º 11 (28 de julio de 2020): 2063–97. http://dx.doi.org/10.1007/s10994-020-05886-4.
Texto completoMiyamoto, Hiroyuki, Jun Morimoto, Kenji Doya y 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 completoTavanaei, Amirhossein, Timothée Masquelier y Anthony Maida. "Representation learning using event-based STDP". Neural Networks 105 (septiembre de 2018): 294–303. http://dx.doi.org/10.1016/j.neunet.2018.05.018.
Texto completoJiao, Pengfei, Hongjiang Chen, Huijun Tang, Qing Bao, Long Zhang, Zhidong Zhao y Huaming Wu. "Contrastive representation learning on dynamic networks". Neural Networks 174 (junio de 2024): 106240. http://dx.doi.org/10.1016/j.neunet.2024.106240.
Texto completoChikwendu, Ijeoma Amuche, Xiaoling Zhang, Isaac Osei Agyemang, Isaac Adjei-Mensah, Ukwuoma Chiagoziem Chima y Chukwuebuka Joseph Ejiyi. "A Comprehensive Survey on Deep Graph Representation Learning Methods". Journal of Artificial Intelligence Research 78 (25 de octubre de 2023): 287–356. http://dx.doi.org/10.1613/jair.1.14768.
Texto completoJurewicz, Mateusz y 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 completoTadepalli, P. y B. K. Natarajan. "A Formal Framework for Speedup Learning from Problems and Solutions". Journal of Artificial Intelligence Research 4 (1 de junio de 1996): 445–75. http://dx.doi.org/10.1613/jair.154.
Texto completoQin, Jisheng, Xiaoqin Zeng, Shengli Wu y Yang Zou. "Context-sensitive graph representation learning". Connection Science 34, n.º 1 (14 de septiembre de 2022): 2313–31. http://dx.doi.org/10.1080/09540091.2022.2115010.
Texto completoAshley, Kevin D. y Edwina L. Rissland. "Law, learning and representation". Artificial Intelligence 150, n.º 1-2 (noviembre de 2003): 17–58. http://dx.doi.org/10.1016/s0004-3702(03)00109-7.
Texto completoAL-Fayyadh, Hayder Rahm Dakheel, Salam Abdulabbas Ganim Ali y Dr Basim Abood. "Modelling an Adaptive Learning System Using Artificial Intelligence". Webology 19, n.º 1 (24 de diciembre de 2021): 01–18. http://dx.doi.org/10.14704/web/v19i1/web19001.
Texto completoMaher, Mary Lou y 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 completoHambadjawa, Johan Agung Pramono y Khaerunnisa. "Development Concept of Artificial Intelligence as an Architect’s Representation: Literature Review". Arsir 8, n.º 1 (22 de marzo de 2024): 14–25. http://dx.doi.org/10.32502/arsir.v8i1.53.
Texto completoWang, Meng-Xiang, Wang-Chien Lee, Tao-Yang Fu y Ge Yu. "On Representation Learning for Road Networks". ACM Transactions on Intelligent Systems and Technology 12, n.º 1 (22 de diciembre de 2020): 1–27. http://dx.doi.org/10.1145/3424346.
Texto completoLu, Run-kun, Jian-wei Liu, Si-ming Lian y Xin Zuo. "Multi-view representation learning in multi-task scene". Neural Computing and Applications 32, n.º 14 (29 de octubre de 2019): 10403–22. http://dx.doi.org/10.1007/s00521-019-04577-z.
Texto completoXie, Ruobing, Stefan Heinrich, Zhiyuan Liu, Cornelius Weber, Yuan Yao, Stefan Wermter y Maosong Sun. "Integrating Image-Based and Knowledge-Based Representation Learning". IEEE Transactions on Cognitive and Developmental Systems 12, n.º 2 (junio de 2020): 169–78. http://dx.doi.org/10.1109/tcds.2019.2906685.
Texto completoSun, Yanan, Hua Mao, Yongsheng Sang y Zhang Yi. "Explicit guiding auto-encoders for learning meaningful representation". Neural Computing and Applications 28, n.º 3 (20 de octubre de 2015): 429–36. http://dx.doi.org/10.1007/s00521-015-2082-x.
Texto completoDietterich, T. G. "Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition". Journal of Artificial Intelligence Research 13 (1 de noviembre de 2000): 227–303. http://dx.doi.org/10.1613/jair.639.
Texto completoKocabas, S. "A review of learning". Knowledge Engineering Review 6, n.º 3 (septiembre de 1991): 195–222. http://dx.doi.org/10.1017/s0269888900005804.
Texto completoO’Mahony, Niall, Sean Campbell, Lenka Krpalkova, Anderson Carvalho, Joseph Walsh y Daniel Riordan. "Representation Learning for Fine-Grained Change Detection". Sensors 21, n.º 13 (30 de junio de 2021): 4486. http://dx.doi.org/10.3390/s21134486.
Texto completoLesort, Timothée, Natalia Díaz-Rodríguez, Jean-Frano̧is Goudou y David Filliat. "State representation learning for control: An overview". Neural Networks 108 (diciembre de 2018): 379–92. http://dx.doi.org/10.1016/j.neunet.2018.07.006.
Texto completoFRANKLIN, JUDY A. y KRYSTAL K. LOCKE. "RECURRENT NEURAL NETWORKS FOR MUSICAL PITCH MEMORY AND CLASSIFICATION". International Journal on Artificial Intelligence Tools 14, n.º 01n02 (febrero de 2005): 329–42. http://dx.doi.org/10.1142/s0218213005002120.
Texto completoShui, Changjian, Boyu Wang y Christian Gagné. "On the benefits of representation regularization in invariance based domain generalization". Machine Learning 111, n.º 3 (1 de enero de 2022): 895–915. http://dx.doi.org/10.1007/s10994-021-06080-w.
Texto completoLi, Fuzhen, Zhenfeng Zhu, Xingxing Zhang, Jian Cheng y Yao Zhao. "Diffusion induced graph representation learning". Neurocomputing 360 (septiembre de 2019): 220–29. http://dx.doi.org/10.1016/j.neucom.2019.06.012.
Texto completoLittman, David y 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 completoZeng, Deyu, Jing Sun, Zongze Wu, Chris Ding y 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 completoMerckling, Astrid, Nicolas Perrin-Gilbert, Alex Coninx y Stéphane Doncieux. "Exploratory State Representation Learning". Frontiers in Robotics and AI 9 (14 de febrero de 2022). http://dx.doi.org/10.3389/frobt.2022.762051.
Texto completoDeshmukh, Aniket Anand, Jayanth Reddy Regatti, Eren Manavoglu y Urun Dogan. "Representation learning for clustering via building consensus". Machine Learning, 9 de septiembre de 2022. http://dx.doi.org/10.1007/s10994-022-06194-9.
Texto completoXu, Lingling, Haoran Xie, Zongxi Li, Fu Lee Wang, Weiming Wang y Qing Li. "Contrastive Learning Models for Sentence Representations". ACM Transactions on Intelligent Systems and Technology, 2 de mayo de 2023. http://dx.doi.org/10.1145/3593590.
Texto completoWang, Yuwei y 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 completoWickstrøm, Kristoffer K., Daniel J. Trosten, Sigurd Løkse, Ahcène Boubekki, Karl øyvind Mikalsen, Michael C. Kampffmeyer y Robert Jenssen. "RELAX: Representation Learning Explainability". International Journal of Computer Vision, 11 de marzo de 2023. http://dx.doi.org/10.1007/s11263-023-01773-2.
Texto completoHiggins, Irina, Sébastien Racanière y 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 completoJeub, Lucas G. S., Giovanni Colavizza, Xiaowen Dong, Marya Bazzi y Mihai Cucuringu. "Local2Global: a distributed approach for scaling representation learning on graphs". Machine Learning, 24 de febrero de 2023. http://dx.doi.org/10.1007/s10994-022-06285-7.
Texto completoOuyang, Tinghui y Xun Shen. "Representation learning based on hybrid polynomial approximated extreme learning machine". Applied Intelligence, 26 de octubre de 2021. http://dx.doi.org/10.1007/s10489-021-02915-0.
Texto completoBorrego-Díaz, Joaquín y Juan Galán Páez. "Knowledge representation for explainable artificial intelligence". Complex & Intelligent Systems, 4 de enero de 2022. http://dx.doi.org/10.1007/s40747-021-00613-5.
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