Artigos de revistas sobre o tema "State representation learning"
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Xu, Cai, Wei Zhao, Jinglong Zhao, Ziyu Guan, Yaming Yang, Long Chen e Xiangyu Song. "Progressive Deep Multi-View Comprehensive Representation Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junho de 2023): 10557–65. http://dx.doi.org/10.1609/aaai.v37i9.26254.
Texto completo da fonteYue, Yang, Bingyi Kang, Zhongwen Xu, Gao Huang e Shuicheng Yan. "Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junho de 2023): 11069–77. http://dx.doi.org/10.1609/aaai.v37i9.26311.
Texto completo da fontede Bruin, Tim, Jens Kober, Karl Tuyls e Robert Babuska. "Integrating State Representation Learning Into Deep Reinforcement Learning". IEEE Robotics and Automation Letters 3, n.º 3 (julho de 2018): 1394–401. http://dx.doi.org/10.1109/lra.2018.2800101.
Texto completo da fonteChen, Haoqiang, Yadong Liu, Zongtan Zhou e Ming Zhang. "A2C: Attention-Augmented Contrastive Learning for State Representation Extraction". Applied Sciences 10, n.º 17 (26 de agosto de 2020): 5902. http://dx.doi.org/10.3390/app10175902.
Texto completo da fonteOng, Sylvie, Yuri Grinberg e Joelle Pineau. "Mixed Observability Predictive State Representations". Proceedings of the AAAI Conference on Artificial Intelligence 27, n.º 1 (30 de junho de 2013): 746–52. http://dx.doi.org/10.1609/aaai.v27i1.8680.
Texto completo da fonteMaier, Marc, Brian Taylor, Huseyin Oktay e David Jensen. "Learning Causal Models of Relational Domains". Proceedings of the AAAI Conference on Artificial Intelligence 24, n.º 1 (3 de julho de 2010): 531–38. http://dx.doi.org/10.1609/aaai.v24i1.7695.
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 fonteChornozhuk, S. "The New Geometric “State-Action” Space Representation for Q-Learning Algorithm for Protein Structure Folding Problem". Cybernetics and Computer Technologies, n.º 3 (27 de outubro de 2020): 59–73. http://dx.doi.org/10.34229/2707-451x.20.3.6.
Texto completo da fonteZhang, Yujia, Lai-Man Po, Xuyuan Xu, Mengyang Liu, Yexin Wang, Weifeng Ou, Yuzhi Zhao e Wing-Yin Yu. "Contrastive Spatio-Temporal Pretext Learning for Self-Supervised Video Representation". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 3 (28 de junho de 2022): 3380–89. http://dx.doi.org/10.1609/aaai.v36i3.20248.
Texto completo da fonteLi, Dongfen, Lichao Meng, Jingjing Li, Ke Lu e Yang Yang. "Domain adaptive state representation alignment for reinforcement learning". Information Sciences 609 (setembro de 2022): 1353–68. http://dx.doi.org/10.1016/j.ins.2022.07.156.
Texto completo da fonteRazmi, Niloufar, e Matthew R. Nassar. "Adaptive Learning through Temporal Dynamics of State Representation". Journal of Neuroscience 42, n.º 12 (1 de fevereiro de 2022): 2524–38. http://dx.doi.org/10.1523/jneurosci.0387-21.2022.
Texto completo da fonteLiu, Qiyuan, Qi Zhou, Rui Yang e Jie Wang. "Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junho de 2023): 8843–51. http://dx.doi.org/10.1609/aaai.v37i7.26063.
Texto completo da fonteJin, Xu, Teng Huang, Ke Wen, Mengxian Chi e Hong An. "HistoSSL: Self-Supervised Representation Learning for Classifying Histopathology Images". Mathematics 11, n.º 1 (26 de dezembro de 2022): 110. http://dx.doi.org/10.3390/math11010110.
Texto completo da fonteLuo, Dezhao, Chang Liu, Yu Zhou, Dongbao Yang, Can Ma, Qixiang Ye e Weiping Wang. "Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 07 (3 de abril de 2020): 11701–8. http://dx.doi.org/10.1609/aaai.v34i07.6840.
Texto completo da fontePark, Deog-Yeong, e Ki-Hoon Lee. "Practical Algorithmic Trading Using State Representation Learning and Imitative Reinforcement Learning". IEEE Access 9 (2021): 152310–21. http://dx.doi.org/10.1109/access.2021.3127209.
Texto completo da fonteChen, Hanxiao. "Robotic Manipulation with Reinforcement Learning, State Representation Learning, and Imitation Learning (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 18 (18 de maio de 2021): 15769–70. http://dx.doi.org/10.1609/aaai.v35i18.17881.
Texto completo da fonteWang, Xingqi, Mengrui Zhang, Bin Chen, Dan Wei e Yanli Shao. "Dynamic Weighted Multitask Learning and Contrastive Learning for Multimodal Sentiment Analysis". Electronics 12, n.º 13 (7 de julho de 2023): 2986. http://dx.doi.org/10.3390/electronics12132986.
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 fonteChang, Xinglong, Jianrong Wang, Rui Guo, Yingkui Wang e Weihao Li. "Asymmetric Graph Contrastive Learning". Mathematics 11, n.º 21 (31 de outubro de 2023): 4505. http://dx.doi.org/10.3390/math11214505.
Texto completo da fonteXing, Jinwei, Takashi Nagata, Kexin Chen, Xinyun Zou, Emre Neftci e Jeffrey L. Krichmar. "Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 12 (18 de maio de 2021): 10452–59. http://dx.doi.org/10.1609/aaai.v35i12.17251.
Texto completo da fonteZhu, Yi, Lei Li e Xindong Wu. "Stacked Convolutional Sparse Auto-Encoders for Representation Learning". ACM Transactions on Knowledge Discovery from Data 15, n.º 2 (abril de 2021): 1–21. http://dx.doi.org/10.1145/3434767.
Texto completo da fonteWang, Sheng, Liyong Chen e Furong Peng. "Multiview Latent Representation Learning with Feature Diversity for Clustering". Mathematical Problems in Engineering 2022 (11 de julho de 2022): 1–12. http://dx.doi.org/10.1155/2022/1866636.
Texto completo da fonteKeller, Patrick, Abdoul Kader Kaboré, Laura Plein, Jacques Klein, Yves Le Traon e Tegawendé F. Bissyandé. "What You See is What it Means! Semantic Representation Learning of Code based on Visualization and Transfer Learning". ACM Transactions on Software Engineering and Methodology 31, n.º 2 (30 de abril de 2022): 1–34. http://dx.doi.org/10.1145/3485135.
Texto completo da fonteSCARPETTA, SILVIA, ZHAOPING LI e JOHN HERTZ. "LEARNING IN AN OSCILLATORY CORTICAL MODEL". Fractals 11, supp01 (fevereiro de 2003): 291–300. http://dx.doi.org/10.1142/s0218348x03001951.
Texto completo da fonteZang, Hongyu, Xin Li e Mingzhong Wang. "SimSR: Simple Distance-Based State Representations for Deep Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 8 (28 de junho de 2022): 8997–9005. http://dx.doi.org/10.1609/aaai.v36i8.20883.
Texto completo da fonteZhu, Zixin, Le Wang, Wei Tang, Ziyi Liu, Nanning Zheng e Gang Hua. "Learning Disentangled Classification and Localization Representations for Temporal Action Localization". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 3 (28 de junho de 2022): 3644–52. http://dx.doi.org/10.1609/aaai.v36i3.20277.
Texto completo da fonteZeng, Fanrui, Yingjie Sun e Yizhou Li. "MRLBot: Multi-Dimensional Representation Learning for Social Media Bot Detection". Electronics 12, n.º 10 (19 de maio de 2023): 2298. http://dx.doi.org/10.3390/electronics12102298.
Texto completo da fonteYang, Di, Yaohui Wang, Quan Kong, Antitza Dantcheva, Lorenzo Garattoni, Gianpiero Francesca e François Brémond. "Self-Supervised Video Representation Learning via Latent Time Navigation". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 3 (26 de junho de 2023): 3118–26. http://dx.doi.org/10.1609/aaai.v37i3.25416.
Texto completo da fonteLi, Xiutian, Siqi Sun e Rui Feng. "Causal Representation Learning via Counterfactual Intervention". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 4 (24 de março de 2024): 3234–42. http://dx.doi.org/10.1609/aaai.v38i4.28108.
Texto completo da fonteKim, Jung-Hoon, Yizhen Zhang, Kuan Han, Zheyu Wen, Minkyu Choi e Zhongming Liu. "Representation learning of resting state fMRI with variational autoencoder". NeuroImage 241 (novembro de 2021): 118423. http://dx.doi.org/10.1016/j.neuroimage.2021.118423.
Texto completo da fonteHumbert, Pierre, Clement Dubost, Julien Audiffren e Laurent Oudre. "Apprenticeship Learning for a Predictive State Representation of Anesthesia". IEEE Transactions on Biomedical Engineering 67, n.º 7 (julho de 2020): 2052–63. http://dx.doi.org/10.1109/tbme.2019.2954348.
Texto completo da fonteLiu, Feng, Ruiming Tang, Xutao Li, Weinan Zhang, Yunming Ye, Haokun Chen, Huifeng Guo, Yuzhou Zhang e Xiuqiang He. "State representation modeling for deep reinforcement learning based recommendation". Knowledge-Based Systems 205 (outubro de 2020): 106170. http://dx.doi.org/10.1016/j.knosys.2020.106170.
Texto completo da fonteMo, Yujie, Liang Peng, Jie Xu, Xiaoshuang Shi e Xiaofeng Zhu. "Simple Unsupervised Graph Representation Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junho de 2022): 7797–805. http://dx.doi.org/10.1609/aaai.v36i7.20748.
Texto completo da fonteAchille, Alessandro, e Stefano Soatto. "A Separation Principle for Control in the Age of Deep Learning". Annual Review of Control, Robotics, and Autonomous Systems 1, n.º 1 (28 de maio de 2018): 287–307. http://dx.doi.org/10.1146/annurev-control-060117-105140.
Texto completo da fonteLi, Zhengyi, Menglu Li, Lida Zhu e Wen Zhang. "Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 1 (24 de março de 2024): 188–96. http://dx.doi.org/10.1609/aaai.v38i1.27770.
Texto completo da fonteGrigoryeva, Lyudmila, Allen Hart e Juan-Pablo Ortega. "Learning strange attractors with reservoir systems". Nonlinearity 36, n.º 9 (27 de julho de 2023): 4674–708. http://dx.doi.org/10.1088/1361-6544/ace492.
Texto completo da fonteKefato, Zekarias, e Sarunas Girdzijauskas. "Gossip and Attend: Context-Sensitive Graph Representation Learning". Proceedings of the International AAAI Conference on Web and Social Media 14 (26 de maio de 2020): 351–59. http://dx.doi.org/10.1609/icwsm.v14i1.7305.
Texto completo da fonteBREEDEN, JOSEPH L., e NORMAN H. PACKARD. "A LEARNING ALGORITHM FOR OPTIMAL REPRESENTATION OF EXPERIMENTAL DATA". International Journal of Bifurcation and Chaos 04, n.º 02 (abril de 1994): 311–26. http://dx.doi.org/10.1142/s0218127494000228.
Texto completo da fonteLiu, Shengli, Xiaowen Zhu, Zewei Cao e Gang Wang. "Deep 1D Landmark Representation Learning for Space Target Pose Estimation". Remote Sensing 14, n.º 16 (18 de agosto de 2022): 4035. http://dx.doi.org/10.3390/rs14164035.
Texto completo da fonteZhang, Jingran, Xing Xu, Fumin Shen, Huimin Lu, Xin Liu e Heng Tao Shen. "Enhancing Audio-Visual Association with Self-Supervised Curriculum Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 4 (18 de maio de 2021): 3351–59. http://dx.doi.org/10.1609/aaai.v35i4.16447.
Texto completo da fonteHan, Ruijiang, Wei Wang, Yuxi Long e Jiajie Peng. "Deep Representation Debiasing via Mutual Information Minimization and Maximization (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junho de 2022): 12965–66. http://dx.doi.org/10.1609/aaai.v36i11.21619.
Texto completo da fonteLi, Fengpeng, Jiabao Li, Wei Han, Ruyi Feng e Lizhe Wang. "Unsupervised Representation High-Resolution Remote Sensing Image Scene Classification via Contrastive Learning Convolutional Neural Network". Photogrammetric Engineering & Remote Sensing 87, n.º 8 (1 de agosto de 2021): 577–91. http://dx.doi.org/10.14358/pers.87.8.577.
Texto completo da fonteHallac, Ibrahim Riza, Betul Ay e Galip Aydin. "User Representation Learning for Social Networks: An Empirical Study". Applied Sciences 11, n.º 12 (13 de junho de 2021): 5489. http://dx.doi.org/10.3390/app11125489.
Texto completo da fonteLiu, Jiexi, e Songcan Chen. "TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 12 (24 de março de 2024): 13918–26. http://dx.doi.org/10.1609/aaai.v38i12.29299.
Texto completo da fontePerrinet, Laurent U. "Role of Homeostasis in Learning Sparse Representations". Neural Computation 22, n.º 7 (julho de 2010): 1812–36. http://dx.doi.org/10.1162/neco.2010.05-08-795.
Texto completo da fonteNaseem, Usman, Imran Razzak, Shah Khalid Khan e Mukesh Prasad. "A Comprehensive Survey on Word Representation Models: From Classical to State-of-the-Art Word Representation Language Models". ACM Transactions on Asian and Low-Resource Language Information Processing 20, n.º 5 (23 de junho de 2021): 1–35. http://dx.doi.org/10.1145/3434237.
Texto completo da fonteJanner, Michael, Karthik Narasimhan e Regina Barzilay. "Representation Learning for Grounded Spatial Reasoning". Transactions of the Association for Computational Linguistics 6 (dezembro de 2018): 49–61. http://dx.doi.org/10.1162/tacl_a_00004.
Texto completo da fonteXu, Xiao, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che e Nan Duan. "BridgeTower: Building Bridges between Encoders in Vision-Language Representation Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junho de 2023): 10637–47. http://dx.doi.org/10.1609/aaai.v37i9.26263.
Texto completo da fonteUmar Jamshaid, Umar Jamshaid. "Optimal Query Execution Plan with Deep Reinforcement Learning". International Journal for Electronic Crime Investigation 5, n.º 3 (6 de abril de 2022): 23–28. http://dx.doi.org/10.54692/ijeci.2022.050386.
Texto completo da fonteGuo, Jifeng, Zhiqi Pang, Wenbo Sun, Shi Li e Yu Chen. "Redundancy Removal Adversarial Active Learning Based on Norm Online Uncertainty Indicator". Computational Intelligence and Neuroscience 2021 (25 de outubro de 2021): 1–10. http://dx.doi.org/10.1155/2021/4752568.
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