Zeitschriftenartikel zum Thema „State representation learning“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit Top-50 Zeitschriftenartikel für die Forschung zum Thema "State representation learning" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Sehen Sie die Zeitschriftenartikel für verschiedene Spezialgebieten durch und erstellen Sie Ihre Bibliographie auf korrekte Weise.
Xu, Cai, Wei Zhao, Jinglong Zhao, Ziyu Guan, Yaming Yang, Long Chen und Xiangyu Song. „Progressive Deep Multi-View Comprehensive Representation Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 9 (26.06.2023): 10557–65. http://dx.doi.org/10.1609/aaai.v37i9.26254.
Der volle Inhalt der QuelleYue, Yang, Bingyi Kang, Zhongwen Xu, Gao Huang und Shuicheng Yan. „Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 9 (26.06.2023): 11069–77. http://dx.doi.org/10.1609/aaai.v37i9.26311.
Der volle Inhalt der Quellede Bruin, Tim, Jens Kober, Karl Tuyls und Robert Babuska. „Integrating State Representation Learning Into Deep Reinforcement Learning“. IEEE Robotics and Automation Letters 3, Nr. 3 (Juli 2018): 1394–401. http://dx.doi.org/10.1109/lra.2018.2800101.
Der volle Inhalt der QuelleChen, Haoqiang, Yadong Liu, Zongtan Zhou und Ming Zhang. „A2C: Attention-Augmented Contrastive Learning for State Representation Extraction“. Applied Sciences 10, Nr. 17 (26.08.2020): 5902. http://dx.doi.org/10.3390/app10175902.
Der volle Inhalt der QuelleOng, Sylvie, Yuri Grinberg und Joelle Pineau. „Mixed Observability Predictive State Representations“. Proceedings of the AAAI Conference on Artificial Intelligence 27, Nr. 1 (30.06.2013): 746–52. http://dx.doi.org/10.1609/aaai.v27i1.8680.
Der volle Inhalt der QuelleMaier, Marc, Brian Taylor, Huseyin Oktay und David Jensen. „Learning Causal Models of Relational Domains“. Proceedings of the AAAI Conference on Artificial Intelligence 24, Nr. 1 (03.07.2010): 531–38. http://dx.doi.org/10.1609/aaai.v24i1.7695.
Der volle Inhalt der QuelleLesort, Timothée, Natalia Díaz-Rodríguez, Jean-Frano̧is Goudou und David Filliat. „State representation learning for control: An overview“. Neural Networks 108 (Dezember 2018): 379–92. http://dx.doi.org/10.1016/j.neunet.2018.07.006.
Der volle Inhalt der QuelleChornozhuk, S. „The New Geometric “State-Action” Space Representation for Q-Learning Algorithm for Protein Structure Folding Problem“. Cybernetics and Computer Technologies, Nr. 3 (27.10.2020): 59–73. http://dx.doi.org/10.34229/2707-451x.20.3.6.
Der volle Inhalt der QuelleZhang, Yujia, Lai-Man Po, Xuyuan Xu, Mengyang Liu, Yexin Wang, Weifeng Ou, Yuzhi Zhao und Wing-Yin Yu. „Contrastive Spatio-Temporal Pretext Learning for Self-Supervised Video Representation“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 3 (28.06.2022): 3380–89. http://dx.doi.org/10.1609/aaai.v36i3.20248.
Der volle Inhalt der QuelleLi, Dongfen, Lichao Meng, Jingjing Li, Ke Lu und Yang Yang. „Domain adaptive state representation alignment for reinforcement learning“. Information Sciences 609 (September 2022): 1353–68. http://dx.doi.org/10.1016/j.ins.2022.07.156.
Der volle Inhalt der QuelleRazmi, Niloufar, und Matthew R. Nassar. „Adaptive Learning through Temporal Dynamics of State Representation“. Journal of Neuroscience 42, Nr. 12 (01.02.2022): 2524–38. http://dx.doi.org/10.1523/jneurosci.0387-21.2022.
Der volle Inhalt der QuelleLiu, Qiyuan, Qi Zhou, Rui Yang und 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, Nr. 7 (26.06.2023): 8843–51. http://dx.doi.org/10.1609/aaai.v37i7.26063.
Der volle Inhalt der QuelleJin, Xu, Teng Huang, Ke Wen, Mengxian Chi und Hong An. „HistoSSL: Self-Supervised Representation Learning for Classifying Histopathology Images“. Mathematics 11, Nr. 1 (26.12.2022): 110. http://dx.doi.org/10.3390/math11010110.
Der volle Inhalt der QuelleLuo, Dezhao, Chang Liu, Yu Zhou, Dongbao Yang, Can Ma, Qixiang Ye und Weiping Wang. „Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 07 (03.04.2020): 11701–8. http://dx.doi.org/10.1609/aaai.v34i07.6840.
Der volle Inhalt der QuellePark, Deog-Yeong, und 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.
Der volle Inhalt der QuelleChen, Hanxiao. „Robotic Manipulation with Reinforcement Learning, State Representation Learning, and Imitation Learning (Student Abstract)“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 18 (18.05.2021): 15769–70. http://dx.doi.org/10.1609/aaai.v35i18.17881.
Der volle Inhalt der QuelleWang, Xingqi, Mengrui Zhang, Bin Chen, Dan Wei und Yanli Shao. „Dynamic Weighted Multitask Learning and Contrastive Learning for Multimodal Sentiment Analysis“. Electronics 12, Nr. 13 (07.07.2023): 2986. http://dx.doi.org/10.3390/electronics12132986.
Der volle Inhalt der QuelleRives, 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, Nr. 15 (05.04.2021): e2016239118. http://dx.doi.org/10.1073/pnas.2016239118.
Der volle Inhalt der QuelleChang, Xinglong, Jianrong Wang, Rui Guo, Yingkui Wang und Weihao Li. „Asymmetric Graph Contrastive Learning“. Mathematics 11, Nr. 21 (31.10.2023): 4505. http://dx.doi.org/10.3390/math11214505.
Der volle Inhalt der QuelleXing, Jinwei, Takashi Nagata, Kexin Chen, Xinyun Zou, Emre Neftci und Jeffrey L. Krichmar. „Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 12 (18.05.2021): 10452–59. http://dx.doi.org/10.1609/aaai.v35i12.17251.
Der volle Inhalt der QuelleZhu, Yi, Lei Li und Xindong Wu. „Stacked Convolutional Sparse Auto-Encoders for Representation Learning“. ACM Transactions on Knowledge Discovery from Data 15, Nr. 2 (April 2021): 1–21. http://dx.doi.org/10.1145/3434767.
Der volle Inhalt der QuelleWang, Sheng, Liyong Chen und Furong Peng. „Multiview Latent Representation Learning with Feature Diversity for Clustering“. Mathematical Problems in Engineering 2022 (11.07.2022): 1–12. http://dx.doi.org/10.1155/2022/1866636.
Der volle Inhalt der QuelleKeller, Patrick, Abdoul Kader Kaboré, Laura Plein, Jacques Klein, Yves Le Traon und 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, Nr. 2 (30.04.2022): 1–34. http://dx.doi.org/10.1145/3485135.
Der volle Inhalt der QuelleSCARPETTA, SILVIA, ZHAOPING LI und JOHN HERTZ. „LEARNING IN AN OSCILLATORY CORTICAL MODEL“. Fractals 11, supp01 (Februar 2003): 291–300. http://dx.doi.org/10.1142/s0218348x03001951.
Der volle Inhalt der QuelleZang, Hongyu, Xin Li und Mingzhong Wang. „SimSR: Simple Distance-Based State Representations for Deep Reinforcement Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 8 (28.06.2022): 8997–9005. http://dx.doi.org/10.1609/aaai.v36i8.20883.
Der volle Inhalt der QuelleZhu, Zixin, Le Wang, Wei Tang, Ziyi Liu, Nanning Zheng und Gang Hua. „Learning Disentangled Classification and Localization Representations for Temporal Action Localization“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 3 (28.06.2022): 3644–52. http://dx.doi.org/10.1609/aaai.v36i3.20277.
Der volle Inhalt der QuelleZeng, Fanrui, Yingjie Sun und Yizhou Li. „MRLBot: Multi-Dimensional Representation Learning for Social Media Bot Detection“. Electronics 12, Nr. 10 (19.05.2023): 2298. http://dx.doi.org/10.3390/electronics12102298.
Der volle Inhalt der QuelleYang, Di, Yaohui Wang, Quan Kong, Antitza Dantcheva, Lorenzo Garattoni, Gianpiero Francesca und François Brémond. „Self-Supervised Video Representation Learning via Latent Time Navigation“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 3 (26.06.2023): 3118–26. http://dx.doi.org/10.1609/aaai.v37i3.25416.
Der volle Inhalt der QuelleLi, Xiutian, Siqi Sun und Rui Feng. „Causal Representation Learning via Counterfactual Intervention“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 4 (24.03.2024): 3234–42. http://dx.doi.org/10.1609/aaai.v38i4.28108.
Der volle Inhalt der QuelleKim, Jung-Hoon, Yizhen Zhang, Kuan Han, Zheyu Wen, Minkyu Choi und Zhongming Liu. „Representation learning of resting state fMRI with variational autoencoder“. NeuroImage 241 (November 2021): 118423. http://dx.doi.org/10.1016/j.neuroimage.2021.118423.
Der volle Inhalt der QuelleHumbert, Pierre, Clement Dubost, Julien Audiffren und Laurent Oudre. „Apprenticeship Learning for a Predictive State Representation of Anesthesia“. IEEE Transactions on Biomedical Engineering 67, Nr. 7 (Juli 2020): 2052–63. http://dx.doi.org/10.1109/tbme.2019.2954348.
Der volle Inhalt der QuelleLiu, Feng, Ruiming Tang, Xutao Li, Weinan Zhang, Yunming Ye, Haokun Chen, Huifeng Guo, Yuzhou Zhang und Xiuqiang He. „State representation modeling for deep reinforcement learning based recommendation“. Knowledge-Based Systems 205 (Oktober 2020): 106170. http://dx.doi.org/10.1016/j.knosys.2020.106170.
Der volle Inhalt der QuelleMo, Yujie, Liang Peng, Jie Xu, Xiaoshuang Shi und Xiaofeng Zhu. „Simple Unsupervised Graph Representation Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 7 (28.06.2022): 7797–805. http://dx.doi.org/10.1609/aaai.v36i7.20748.
Der volle Inhalt der QuelleAchille, Alessandro, und Stefano Soatto. „A Separation Principle for Control in the Age of Deep Learning“. Annual Review of Control, Robotics, and Autonomous Systems 1, Nr. 1 (28.05.2018): 287–307. http://dx.doi.org/10.1146/annurev-control-060117-105140.
Der volle Inhalt der QuelleLi, Zhengyi, Menglu Li, Lida Zhu und 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, Nr. 1 (24.03.2024): 188–96. http://dx.doi.org/10.1609/aaai.v38i1.27770.
Der volle Inhalt der QuelleGrigoryeva, Lyudmila, Allen Hart und Juan-Pablo Ortega. „Learning strange attractors with reservoir systems“. Nonlinearity 36, Nr. 9 (27.07.2023): 4674–708. http://dx.doi.org/10.1088/1361-6544/ace492.
Der volle Inhalt der QuelleKefato, Zekarias, und Sarunas Girdzijauskas. „Gossip and Attend: Context-Sensitive Graph Representation Learning“. Proceedings of the International AAAI Conference on Web and Social Media 14 (26.05.2020): 351–59. http://dx.doi.org/10.1609/icwsm.v14i1.7305.
Der volle Inhalt der QuelleBREEDEN, JOSEPH L., und NORMAN H. PACKARD. „A LEARNING ALGORITHM FOR OPTIMAL REPRESENTATION OF EXPERIMENTAL DATA“. International Journal of Bifurcation and Chaos 04, Nr. 02 (April 1994): 311–26. http://dx.doi.org/10.1142/s0218127494000228.
Der volle Inhalt der QuelleLiu, Shengli, Xiaowen Zhu, Zewei Cao und Gang Wang. „Deep 1D Landmark Representation Learning for Space Target Pose Estimation“. Remote Sensing 14, Nr. 16 (18.08.2022): 4035. http://dx.doi.org/10.3390/rs14164035.
Der volle Inhalt der QuelleZhang, Jingran, Xing Xu, Fumin Shen, Huimin Lu, Xin Liu und Heng Tao Shen. „Enhancing Audio-Visual Association with Self-Supervised Curriculum Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 4 (18.05.2021): 3351–59. http://dx.doi.org/10.1609/aaai.v35i4.16447.
Der volle Inhalt der QuelleHan, Ruijiang, Wei Wang, Yuxi Long und Jiajie Peng. „Deep Representation Debiasing via Mutual Information Minimization and Maximization (Student Abstract)“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 11 (28.06.2022): 12965–66. http://dx.doi.org/10.1609/aaai.v36i11.21619.
Der volle Inhalt der QuelleLi, Fengpeng, Jiabao Li, Wei Han, Ruyi Feng und Lizhe Wang. „Unsupervised Representation High-Resolution Remote Sensing Image Scene Classification via Contrastive Learning Convolutional Neural Network“. Photogrammetric Engineering & Remote Sensing 87, Nr. 8 (01.08.2021): 577–91. http://dx.doi.org/10.14358/pers.87.8.577.
Der volle Inhalt der QuelleHallac, Ibrahim Riza, Betul Ay und Galip Aydin. „User Representation Learning for Social Networks: An Empirical Study“. Applied Sciences 11, Nr. 12 (13.06.2021): 5489. http://dx.doi.org/10.3390/app11125489.
Der volle Inhalt der QuelleLiu, Jiexi, und Songcan Chen. „TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 12 (24.03.2024): 13918–26. http://dx.doi.org/10.1609/aaai.v38i12.29299.
Der volle Inhalt der QuellePerrinet, Laurent U. „Role of Homeostasis in Learning Sparse Representations“. Neural Computation 22, Nr. 7 (Juli 2010): 1812–36. http://dx.doi.org/10.1162/neco.2010.05-08-795.
Der volle Inhalt der QuelleNaseem, Usman, Imran Razzak, Shah Khalid Khan und 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, Nr. 5 (23.06.2021): 1–35. http://dx.doi.org/10.1145/3434237.
Der volle Inhalt der QuelleJanner, Michael, Karthik Narasimhan und Regina Barzilay. „Representation Learning for Grounded Spatial Reasoning“. Transactions of the Association for Computational Linguistics 6 (Dezember 2018): 49–61. http://dx.doi.org/10.1162/tacl_a_00004.
Der volle Inhalt der QuelleXu, Xiao, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che und Nan Duan. „BridgeTower: Building Bridges between Encoders in Vision-Language Representation Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 9 (26.06.2023): 10637–47. http://dx.doi.org/10.1609/aaai.v37i9.26263.
Der volle Inhalt der QuelleUmar Jamshaid, Umar Jamshaid. „Optimal Query Execution Plan with Deep Reinforcement Learning“. International Journal for Electronic Crime Investigation 5, Nr. 3 (06.04.2022): 23–28. http://dx.doi.org/10.54692/ijeci.2022.050386.
Der volle Inhalt der QuelleGuo, Jifeng, Zhiqi Pang, Wenbo Sun, Shi Li und Yu Chen. „Redundancy Removal Adversarial Active Learning Based on Norm Online Uncertainty Indicator“. Computational Intelligence and Neuroscience 2021 (25.10.2021): 1–10. http://dx.doi.org/10.1155/2021/4752568.
Der volle Inhalt der Quelle