Articoli di riviste sul 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. 3 (15 settembre 2020): 1–159. http://dx.doi.org/10.2200/s01045ed1v01y202009aim046.
Konidaris, 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 gennaio 2018): 215–89. http://dx.doi.org/10.1613/jair.5575.
Rezayi, Saed. "Learning Better Representations Using Auxiliary Knowledge". Proceedings of the AAAI Conference on Artificial Intelligence 37, n. 13 (26 giugno 2023): 16133–34. http://dx.doi.org/10.1609/aaai.v37i13.26927.
FROMMBERGER, LUTZ. "LEARNING TO BEHAVE IN SPACE: A QUALITATIVE SPATIAL REPRESENTATION FOR ROBOT NAVIGATION WITH REINFORCEMENT LEARNING". International Journal on Artificial Intelligence Tools 17, n. 03 (giugno 2008): 465–82. http://dx.doi.org/10.1142/s021821300800400x.
Haghir Chehreghani, Morteza, e Mostafa Haghir Chehreghani. "Learning representations from dendrograms". Machine Learning 109, n. 9-10 (16 agosto 2020): 1779–802. http://dx.doi.org/10.1007/s10994-020-05895-3.
Saitta, Lorenza. "Representation change in machine learning". AI Communications 9, n. 1 (1996): 14–20. http://dx.doi.org/10.3233/aic-1996-9102.
Rives, 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. 15 (5 aprile 2021): e2016239118. http://dx.doi.org/10.1073/pnas.2016239118.
Kang, Zhao, Xiao Lu, Jian Liang, Kun Bai e Zenglin Xu. "Relation-Guided Representation Learning". Neural Networks 131 (novembre 2020): 93–102. http://dx.doi.org/10.1016/j.neunet.2020.07.014.
Prorok, Máté. "Applications of artificial intelligence systems". Deliberationes 15, Különszám (2022): 76–88. http://dx.doi.org/10.54230/delib.2022.k.sz.76.
Mazoure, 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 ottobre 2022): 597–636. http://dx.doi.org/10.1613/jair.1.13854.
Lawler, Robert W. "Getting Intelligence Into the Minds of People". LEARNing Landscapes 6, n. 2 (2 giugno 2013): 223–47. http://dx.doi.org/10.36510/learnland.v6i2.614.
Koohzadi, Maryam, Nasrollah Moghadam Charkari e Foad Ghaderi. "Unsupervised representation learning based on the deep multi-view ensemble learning". Applied Intelligence 50, n. 2 (31 luglio 2019): 562–81. http://dx.doi.org/10.1007/s10489-019-01526-0.
Hanson, Stephen José, e David J. Burr. "What connectionist models learn: Learning and representation in connectionist networks". Behavioral and Brain Sciences 13, n. 3 (settembre 1990): 471–89. http://dx.doi.org/10.1017/s0140525x00079760.
Zheng, 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. 1 (10 agosto 2020): 248–64. http://dx.doi.org/10.1007/s10489-020-01821-1.
Li, Bentian, e Dechang Pi. "Network representation learning: a systematic literature review". Neural Computing and Applications 32, n. 21 (20 aprile 2020): 16647–79. http://dx.doi.org/10.1007/s00521-020-04908-5.
Huang, 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. 5 (13 febbraio 2019): 747–63. http://dx.doi.org/10.1007/s10994-019-05783-5.
Haghir Chehreghani, Morteza. "Unsupervised representation learning with Minimax distance measures". Machine Learning 109, n. 11 (28 luglio 2020): 2063–97. http://dx.doi.org/10.1007/s10994-020-05886-4.
Miyamoto, Hiroyuki, Jun Morimoto, Kenji Doya e Mitsuo Kawato. "Reinforcement learning with via-point representation". Neural Networks 17, n. 3 (aprile 2004): 299–305. http://dx.doi.org/10.1016/j.neunet.2003.11.004.
Tavanaei, Amirhossein, Timothée Masquelier e Anthony Maida. "Representation learning using event-based STDP". Neural Networks 105 (settembre 2018): 294–303. http://dx.doi.org/10.1016/j.neunet.2018.05.018.
Jiao, Pengfei, Hongjiang Chen, Huijun Tang, Qing Bao, Long Zhang, Zhidong Zhao e Huaming Wu. "Contrastive representation learning on dynamic networks". Neural Networks 174 (giugno 2024): 106240. http://dx.doi.org/10.1016/j.neunet.2024.106240.
Chikwendu, 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 ottobre 2023): 287–356. http://dx.doi.org/10.1613/jair.1.14768.
Jurewicz, Mateusz, e Leon Derczynski. "Set-to-Sequence Methods in Machine Learning: A Review". Journal of Artificial Intelligence Research 71 (12 agosto 2021): 885–924. http://dx.doi.org/10.1613/jair.1.12839.
Tadepalli, P., e B. K. Natarajan. "A Formal Framework for Speedup Learning from Problems and Solutions". Journal of Artificial Intelligence Research 4 (1 giugno 1996): 445–75. http://dx.doi.org/10.1613/jair.154.
Qin, Jisheng, Xiaoqin Zeng, Shengli Wu e Yang Zou. "Context-sensitive graph representation learning". Connection Science 34, n. 1 (14 settembre 2022): 2313–31. http://dx.doi.org/10.1080/09540091.2022.2115010.
Ashley, Kevin D., e Edwina L. Rissland. "Law, learning and representation". Artificial Intelligence 150, n. 1-2 (novembre 2003): 17–58. http://dx.doi.org/10.1016/s0004-3702(03)00109-7.
AL-Fayyadh, Hayder Rahm Dakheel, Salam Abdulabbas Ganim Ali e Dr Basim Abood. "Modelling an Adaptive Learning System Using Artificial Intelligence". Webology 19, n. 1 (24 dicembre 2021): 01–18. http://dx.doi.org/10.14704/web/v19i1/web19001.
Maher, Mary Lou, e Heng Li. "Learning design concepts using machine learning techniques". Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8, n. 2 (1994): 95–111. http://dx.doi.org/10.1017/s0890060400000706.
Hambadjawa, Johan Agung Pramono, e Khaerunnisa. "Development Concept of Artificial Intelligence as an Architect’s Representation: Literature Review". Arsir 8, n. 1 (22 marzo 2024): 14–25. http://dx.doi.org/10.32502/arsir.v8i1.53.
Wang, 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. 1 (22 dicembre 2020): 1–27. http://dx.doi.org/10.1145/3424346.
Lu, 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. 14 (29 ottobre 2019): 10403–22. http://dx.doi.org/10.1007/s00521-019-04577-z.
Xie, 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. 2 (giugno 2020): 169–78. http://dx.doi.org/10.1109/tcds.2019.2906685.
Sun, Yanan, Hua Mao, Yongsheng Sang e Zhang Yi. "Explicit guiding auto-encoders for learning meaningful representation". Neural Computing and Applications 28, n. 3 (20 ottobre 2015): 429–36. http://dx.doi.org/10.1007/s00521-015-2082-x.
Dietterich, T. G. "Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition". Journal of Artificial Intelligence Research 13 (1 novembre 2000): 227–303. http://dx.doi.org/10.1613/jair.639.
Kocabas, S. "A review of learning". Knowledge Engineering Review 6, n. 3 (settembre 1991): 195–222. http://dx.doi.org/10.1017/s0269888900005804.
O’Mahony, Niall, Sean Campbell, Lenka Krpalkova, Anderson Carvalho, Joseph Walsh e Daniel Riordan. "Representation Learning for Fine-Grained Change Detection". Sensors 21, n. 13 (30 giugno 2021): 4486. http://dx.doi.org/10.3390/s21134486.
Lesort, Timothée, Natalia Díaz-Rodríguez, Jean-Frano̧is Goudou e David Filliat. "State representation learning for control: An overview". Neural Networks 108 (dicembre 2018): 379–92. http://dx.doi.org/10.1016/j.neunet.2018.07.006.
FRANKLIN, JUDY A., e KRYSTAL K. LOCKE. "RECURRENT NEURAL NETWORKS FOR MUSICAL PITCH MEMORY AND CLASSIFICATION". International Journal on Artificial Intelligence Tools 14, n. 01n02 (febbraio 2005): 329–42. http://dx.doi.org/10.1142/s0218213005002120.
Shui, Changjian, Boyu Wang e Christian Gagné. "On the benefits of representation regularization in invariance based domain generalization". Machine Learning 111, n. 3 (1 gennaio 2022): 895–915. http://dx.doi.org/10.1007/s10994-021-06080-w.
Li, Fuzhen, Zhenfeng Zhu, Xingxing Zhang, Jian Cheng e Yao Zhao. "Diffusion induced graph representation learning". Neurocomputing 360 (settembre 2019): 220–29. http://dx.doi.org/10.1016/j.neucom.2019.06.012.
Littman, David, e Maarten van Someren. "International Workshop on Knowledge Representation and Organization in Machine Learning". AI Communications 1, n. 1 (1988): 44–45. http://dx.doi.org/10.3233/aic-1988-1108.
Zeng, Deyu, Jing Sun, Zongze Wu, Chris Ding e Zhigang Ren. "Data representation learning via dictionary learning and self-representation". Applied Intelligence, 31 agosto 2023. http://dx.doi.org/10.1007/s10489-023-04902-z.
Merckling, Astrid, Nicolas Perrin-Gilbert, Alex Coninx e Stéphane Doncieux. "Exploratory State Representation Learning". Frontiers in Robotics and AI 9 (14 febbraio 2022). http://dx.doi.org/10.3389/frobt.2022.762051.
Deshmukh, Aniket Anand, Jayanth Reddy Regatti, Eren Manavoglu e Urun Dogan. "Representation learning for clustering via building consensus". Machine Learning, 9 settembre 2022. http://dx.doi.org/10.1007/s10994-022-06194-9.
Xu, 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 maggio 2023. http://dx.doi.org/10.1145/3593590.
Wang, Yuwei, e Yi Zeng. "Statistical Analysis of Multisensory and Text-Derived Representations on Concept Learning". Frontiers in Computational Neuroscience 16 (27 aprile 2022). http://dx.doi.org/10.3389/fncom.2022.861265.
Wickstrøm, 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 marzo 2023. http://dx.doi.org/10.1007/s11263-023-01773-2.
Higgins, Irina, Sébastien Racanière e Danilo Rezende. "Symmetry-Based Representations for Artificial and Biological General Intelligence". Frontiers in Computational Neuroscience 16 (14 aprile 2022). http://dx.doi.org/10.3389/fncom.2022.836498.
Jeub, 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 febbraio 2023. http://dx.doi.org/10.1007/s10994-022-06285-7.
Ouyang, Tinghui, e Xun Shen. "Representation learning based on hybrid polynomial approximated extreme learning machine". Applied Intelligence, 26 ottobre 2021. http://dx.doi.org/10.1007/s10489-021-02915-0.
Borrego-Díaz, Joaquín, e Juan Galán Páez. "Knowledge representation for explainable artificial intelligence". Complex & Intelligent Systems, 4 gennaio 2022. http://dx.doi.org/10.1007/s40747-021-00613-5.