Journal articles on the topic 'States representation learning'
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Konidaris, George, Leslie Pack Kaelbling, and Tomas Lozano-Perez. "From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning." Journal of Artificial Intelligence Research 61 (January 31, 2018): 215–89. http://dx.doi.org/10.1613/jair.5575.
Full textSCARPETTA, SILVIA, ZHAOPING LI, and JOHN HERTZ. "LEARNING IN AN OSCILLATORY CORTICAL MODEL." Fractals 11, supp01 (February 2003): 291–300. http://dx.doi.org/10.1142/s0218348x03001951.
Full textZhu, Zheng-Mao, Shengyi Jiang, Yu-Ren Liu, Yang Yu, and Kun Zhang. "Invariant Action Effect Model for Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 9260–68. http://dx.doi.org/10.1609/aaai.v36i8.20913.
Full textYue, Yang, Bingyi Kang, Zhongwen Xu, Gao Huang, and Shuicheng Yan. "Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 11069–77. http://dx.doi.org/10.1609/aaai.v37i9.26311.
Full textChornozhuk, S. "The New Geometric “State-Action” Space Representation for Q-Learning Algorithm for Protein Structure Folding Problem." Cybernetics and Computer Technologies, no. 3 (October 27, 2020): 59–73. http://dx.doi.org/10.34229/2707-451x.20.3.6.
Full textLamanna, Leonardo, Alfonso Emilio Gerevini, Alessandro Saetti, Luciano Serafini, and Paolo Traverso. "On-line Learning of Planning Domains from Sensor Data in PAL: Scaling up to Large State Spaces." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11862–69. http://dx.doi.org/10.1609/aaai.v35i13.17409.
Full textSapena, Oscar, Eva Onaindia, and Eliseo Marzal. "Automated feature extraction for planning state representation." Inteligencia Artificial 27, no. 74 (October 10, 2024): 227–42. http://dx.doi.org/10.4114/intartif.vol27iss74pp227-242.
Full textO’Donnell, Ryan, and John Wright. "Learning and testing quantum states via probabilistic combinatorics and representation theory." Current Developments in Mathematics 2021, no. 1 (2021): 43–94. http://dx.doi.org/10.4310/cdm.2021.v2021.n1.a2.
Full textZhang, Hengyuan, Suyao Zhao, Ruiheng Liu, Wenlong Wang, Yixin Hong, and Runjiu Hu. "Automatic Traffic Anomaly Detection on the Road Network with Spatial-Temporal Graph Neural Network Representation Learning." Wireless Communications and Mobile Computing 2022 (June 20, 2022): 1–12. http://dx.doi.org/10.1155/2022/4222827.
Full textDayan, Peter. "Improving Generalization for Temporal Difference Learning: The Successor Representation." Neural Computation 5, no. 4 (July 1993): 613–24. http://dx.doi.org/10.1162/neco.1993.5.4.613.
Full textGershman, Samuel J., Christopher D. Moore, Michael T. Todd, Kenneth A. Norman, and Per B. Sederberg. "The Successor Representation and Temporal Context." Neural Computation 24, no. 6 (June 2012): 1553–68. http://dx.doi.org/10.1162/neco_a_00282.
Full textM. Mounika, L. Sahithi, K. Prasanna Lakshmi, K. Praveenya, and N. Ashok Kumar. "Quantum driven deep learning for enhanced diabetic retinopathy detection." World Journal of Advanced Research and Reviews 22, no. 1 (April 30, 2024): 055–60. http://dx.doi.org/10.30574/wjarr.2024.22.1.0964.
Full textRobins, Anthony V. "MULTIPLE REPRESENTATIONS IN CONNECTIONIST SYSTEMS." International Journal of Neural Systems 02, no. 04 (January 1991): 345–62. http://dx.doi.org/10.1142/s0129065791000327.
Full textLi, Xinlin, Changhe Fan, and Chengyue Su. "Self-Supervised Learning for Speech-Based Detection of Depressive States." Frontiers in Computing and Intelligent Systems 11, no. 2 (February 27, 2025): 106–9. https://doi.org/10.54097/1cspmj65.
Full textWu, Bo, Yan Peng Feng, and Hong Yan Zheng. "A Model-Based Factored Bayesian Reinforcement Learning Approach." Applied Mechanics and Materials 513-517 (February 2014): 1092–95. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1092.
Full textAlvi, Maira, Tim French, Philip Keymer, and Rachel Cardell-Oliver. "Automated State Estimation for Summarizing the Dynamics of Complex Urban Systems Using Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23020–26. http://dx.doi.org/10.1609/aaai.v38i21.30344.
Full textYamashita, Kodai, and Tomoki Hamagami. "Reinforcement Learning for POMDP Environments Using State Representation with Reservoir Computing." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 4 (July 20, 2022): 562–69. http://dx.doi.org/10.20965/jaciii.2022.p0562.
Full textYan, Yan, Xu-Cheng Yin, Sujian Li, Mingyuan Yang, and Hong-Wei Hao. "Learning Document Semantic Representation with Hybrid Deep Belief Network." Computational Intelligence and Neuroscience 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/650527.
Full textZeng, Zheng, Rodney M. Goodman, and Padhraic Smyth. "Learning Finite State Machines With Self-Clustering Recurrent Networks." Neural Computation 5, no. 6 (November 1993): 976–90. http://dx.doi.org/10.1162/neco.1993.5.6.976.
Full textBrantley, Kianté, Soroush Mehri, and Geoff J. Gordon. "Successor Feature Sets: Generalizing Successor Representations Across Policies." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11774–81. http://dx.doi.org/10.1609/aaai.v35i13.17399.
Full textSupianto, Ahmad Afif, Satrio Agung Wicaksono, Fitra A. Bachtiar, Admaja Dwi Herlambang, Yusuke Hayashi, and Tsukasa Hirashima. "Web-based Application for Visual Representation of Learners' Problem-Posing Learning Pattern." Journal of Information Technology and Computer Science 4, no. 1 (June 27, 2019): 103. http://dx.doi.org/10.25126/jitecs.20194172.
Full textJanowicz, Maciej, and Andrzej Zembrzuski. "Guessing quantum states from images of their zeros in the complex plane." Machine Graphics and Vision 32, no. 3/4 (December 18, 2023): 147–59. http://dx.doi.org/10.22630/mgv.2022.31.3.8.
Full textARENA, PAOLO, LUIGI FORTUNA, MATTIA FRASCA, DAVIDE LOMBARDO, LUCA PATANÈ, and PAOLO CRUCITTI. "TURING PATTERNS IN RD-CNNs FOR THE EMERGENCE OF PERCEPTUAL STATES IN ROVING ROBOTS." International Journal of Bifurcation and Chaos 17, no. 01 (January 2007): 107–27. http://dx.doi.org/10.1142/s0218127407017203.
Full textHadra, Mohammad, and Iman Abdelrahman. "Automatic EEG-based Alertness Classification using Sparse Representation and Dictionary Learning." Journal of Biomedical Engineering and Medical Imaging 7, no. 5 (November 8, 2020): 19–28. http://dx.doi.org/10.14738/jbemi.75.9264.
Full textDe Giacomo, Giuseppe, Marco Favorito, Luca Iocchi, and Fabio Patrizi. "Imitation Learning over Heterogeneous Agents with Restraining Bolts." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 517–21. http://dx.doi.org/10.1609/icaps.v30i1.6747.
Full textBenjamin, Ari S., and Konrad P. Kording. "A role for cortical interneurons as adversarial discriminators." PLOS Computational Biology 19, no. 9 (September 28, 2023): e1011484. http://dx.doi.org/10.1371/journal.pcbi.1011484.
Full textGao, Kaizhi, Tianyu Wang, Zhongjing Ma, and Suli Zou. "Winnie: Task-Oriented Dialog System with Structure-Aware Contrastive Learning and Enhanced Policy Planning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (March 24, 2024): 18021–29. http://dx.doi.org/10.1609/aaai.v38i16.29758.
Full textMontero Quispe, Kevin G., Daniel M. S. Utyiama, Eulanda M. dos Santos, Horácio A. B. F. Oliveira, and Eduardo J. P. Souto. "Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals." Sensors 22, no. 23 (November 23, 2022): 9102. http://dx.doi.org/10.3390/s22239102.
Full textNiu, Yijie, Wu Deng, Xuesong Zhang, Yuchun Wang, Guoqing Wang, Yanjuan Wang, and Pengpeng Zhi. "A Sparse Learning Method with Regularization Parameter as a Self-Adaptation Strategy for Rolling Bearing Fault Diagnosis." Electronics 12, no. 20 (October 16, 2023): 4282. http://dx.doi.org/10.3390/electronics12204282.
Full textCai, Yuanying, Chuheng Zhang, Wei Shen, Xuyun Zhang, Wenjie Ruan, and Longbo Huang. "RePreM: Representation Pre-training with Masked Model for Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 6879–87. http://dx.doi.org/10.1609/aaai.v37i6.25842.
Full textBOXER, PAUL A. "LEARNING NAIVE PHYSICS BY VISUAL OBSERVATION: USING QUALITATIVE SPATIAL REPRESENTATIONS AND PROBABILISTIC REASONING." International Journal of Computational Intelligence and Applications 01, no. 03 (September 2001): 273–85. http://dx.doi.org/10.1142/s146902680100024x.
Full textLanchantin, Jack, Sainbayar Sukhbaatar, Gabriel Synnaeve, Yuxuan Sun, Kavya Srinet, and Arthur Szlam. "A Data Source for Reasoning Embodied Agents." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8438–46. http://dx.doi.org/10.1609/aaai.v37i7.26017.
Full textZhou, Hangbo, Gang Zhang, and Yong-Wei Zhang. "Neural network representation and optimization of thermoelectric states of multiple interacting quantum dots." Physical Chemistry Chemical Physics 22, no. 28 (2020): 16165–73. http://dx.doi.org/10.1039/d0cp02894k.
Full textYu, Jia, Huiling Peng, Guoqiang Wang, and Nianfeng Shi. "A topical VAEGAN-IHMM approach for automatic story segmentation." Mathematical Biosciences and Engineering 21, no. 7 (2024): 6608–30. http://dx.doi.org/10.3934/mbe.2024289.
Full textAnggraini, Nanda Ayu, Eka Fitria Ningsih, Choirudin Choirudin, Rani Darmayanti, and Diyan Triyanto. "Application of the AIR learning model using song media to improve students’ mathematical representational ability." AMCA Journal of Science and Technology 2, no. 1 (November 11, 2022): 28–33. http://dx.doi.org/10.51773/ajst.v2i1.264.
Full textHennig, Jay A., Sandra A. Romero Pinto, Takahiro Yamaguchi, Scott W. Linderman, Naoshige Uchida, and Samuel J. Gershman. "Emergence of belief-like representations through reinforcement learning." PLOS Computational Biology 19, no. 9 (September 11, 2023): e1011067. http://dx.doi.org/10.1371/journal.pcbi.1011067.
Full textFrancois-Lavet, Vincent, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, and Raphael Fonteneau. "On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability." Journal of Artificial Intelligence Research 65 (May 5, 2019): 1–30. http://dx.doi.org/10.1613/jair.1.11478.
Full textLiao, Weijian, Zongzhang Zhang, and Yang Yu. "Policy-Independent Behavioral Metric-Based Representation for Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8746–54. http://dx.doi.org/10.1609/aaai.v37i7.26052.
Full textCorli, Sebastiano, Lorenzo Moro, Davide E. Galli, and Enrico Prati. "Casting Rubik’s Group into a Unitary Representation for Reinforcement Learning." Journal of Physics: Conference Series 2533, no. 1 (June 1, 2023): 012006. http://dx.doi.org/10.1088/1742-6596/2533/1/012006.
Full textHirshorn, Elizabeth A., Yuanning Li, Michael J. Ward, R. Mark Richardson, Julie A. Fiez, and Avniel Singh Ghuman. "Decoding and disrupting left midfusiform gyrus activity during word reading." Proceedings of the National Academy of Sciences 113, no. 29 (June 20, 2016): 8162–67. http://dx.doi.org/10.1073/pnas.1604126113.
Full textCresswell, Stephen, and Peter Gregory. "Generalised Domain Model Acquisition from Action Traces." Proceedings of the International Conference on Automated Planning and Scheduling 21 (March 22, 2011): 42–49. http://dx.doi.org/10.1609/icaps.v21i1.13476.
Full textCharalambous, Panayiotis, Julien Pettre, Vassilis Vassiliades, Yiorgos Chrysanthou, and Nuria Pelechano. "GREIL-Crowds: Crowd Simulation with Deep Reinforcement Learning and Examples." ACM Transactions on Graphics 42, no. 4 (July 26, 2023): 1–15. http://dx.doi.org/10.1145/3592459.
Full textBARRETO, GUILHERME DE A., and ALUIZIO F. R. ARAÚJO. "Unsupervised Learning and Recall of Temporal Sequences: An Application to Robotics." International Journal of Neural Systems 09, no. 03 (June 1999): 235–42. http://dx.doi.org/10.1142/s012906579900023x.
Full textZou, Eric, Erik Long, and Erhai Zhao. "Learning a compass spin model with neural network quantum states." Journal of Physics: Condensed Matter 34, no. 12 (January 7, 2022): 125802. http://dx.doi.org/10.1088/1361-648x/ac43ff.
Full textCASTELLANO, GIOVANNA, CIRO CASTIELLO, DANILO DELL'AGNELLO, ANNA MARIA FANELLI, CORRADO MENCAR, and MARIA ALESSANDRA TORSELLO. "LEARNING FUZZY USER PROFILES FOR RESOURCE RECOMMENDATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 04 (August 2010): 389–410. http://dx.doi.org/10.1142/s0218488510006611.
Full textGuo, Chao, and Rongrong Ren. "Learning and Problem Representation in Foreign Policy Decision-Making: China'S Decision to Enter the Korean War Revisited." Public Administration Quarterly 27, no. 3 (September 2003): 274–310. http://dx.doi.org/10.1177/073491490302700302.
Full textTrevarthen, Colwyn, and Kenneth J. Aitken. "Brain development, infant communication, and empathy disorders: Intrinsic factors in child mental health." Development and Psychopathology 6, no. 4 (1994): 597–633. http://dx.doi.org/10.1017/s0954579400004703.
Full textWhitehead, Steven D., and Dana H. Ballard. "Active Perception and Reinforcement Learning." Neural Computation 2, no. 4 (December 1990): 409–19. http://dx.doi.org/10.1162/neco.1990.2.4.409.
Full textTian, Yuan. "Music emotion representation based on non-negative matrix factorization algorithm and user label information." PeerJ Computer Science 9 (September 25, 2023): e1590. http://dx.doi.org/10.7717/peerj-cs.1590.
Full textFinn, Tobias Sebastian, Lucas Disson, Alban Farchi, Marc Bocquet, and Charlotte Durand. "Representation learning with unconditional denoising diffusion models for dynamical systems." Nonlinear Processes in Geophysics 31, no. 3 (September 19, 2024): 409–31. http://dx.doi.org/10.5194/npg-31-409-2024.
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