Artigos de revistas sobre o tema "Dynamic Representation Learning"
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Lee, Jungmin, e Wongyoung Lee. "Aspects of A Study on the Multi Presentational Metaphor Education Using Online Telestration". Korean Society of Culture and Convergence 44, n.º 9 (30 de setembro de 2022): 163–73. http://dx.doi.org/10.33645/cnc.2022.9.44.9.163.
Texto completo da fonteBiswal, Siddharth, Cao Xiao, Lucas M. Glass, Elizabeth Milkovits e Jimeng Sun. "Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 01 (3 de abril de 2020): 557–64. http://dx.doi.org/10.1609/aaai.v34i01.5394.
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 fonteGoyal, Palash, Sujit Rokka Chhetri e Arquimedes Canedo. "dyngraph2vec: Capturing network dynamics using dynamic graph representation learning". Knowledge-Based Systems 187 (janeiro de 2020): 104816. http://dx.doi.org/10.1016/j.knosys.2019.06.024.
Texto completo da fonteHan, Liangzhe, Ruixing Zhang, Leilei Sun, Bowen Du, Yanjie Fu e Tongyu Zhu. "Generic and Dynamic Graph Representation Learning for Crowd Flow Modeling". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 4 (26 de junho de 2023): 4293–301. http://dx.doi.org/10.1609/aaai.v37i4.25548.
Texto completo da fonteJiao, Pengfei, Hongjiang Chen, Huijun Tang, Qing Bao, Long Zhang, Zhidong Zhao e Huaming Wu. "Contrastive representation learning on dynamic networks". Neural Networks 174 (junho de 2024): 106240. http://dx.doi.org/10.1016/j.neunet.2024.106240.
Texto completo da fonteRadulescu, Angela, Yeon Soon Shin e Yael Niv. "Human Representation Learning". Annual Review of Neuroscience 44, n.º 1 (8 de julho de 2021): 253–73. http://dx.doi.org/10.1146/annurev-neuro-092920-120559.
Texto completo da fonteLiu, Dianbo, Alex Lamb, Xu Ji, Pascal Junior Tikeng Notsawo, Michael Mozer, Yoshua Bengio e Kenji Kawaguchi. "Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junho de 2023): 8825–33. http://dx.doi.org/10.1609/aaai.v37i7.26061.
Texto completo da fonteDeng, Yongjian, Hao Chen e Youfu Li. "A Dynamic GCN with Cross-Representation Distillation for Event-Based Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 2 (24 de março de 2024): 1492–500. http://dx.doi.org/10.1609/aaai.v38i2.27914.
Texto completo da fonteLi, Jintang, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu e Changhua Meng. "Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junho de 2023): 8588–96. http://dx.doi.org/10.1609/aaai.v37i7.26034.
Texto completo da fonteWei, Hao, Guyu Hu, Wei Bai, Shiming Xia e Zhisong Pan. "Lifelong representation learning in dynamic attributed networks". Neurocomputing 358 (setembro de 2019): 1–9. http://dx.doi.org/10.1016/j.neucom.2019.05.038.
Texto completo da fonteLee, Dongha, Xiaoqian Jiang e Hwanjo Yu. "Harmonized representation learning on dynamic EHR graphs". Journal of Biomedical Informatics 106 (junho de 2020): 103426. http://dx.doi.org/10.1016/j.jbi.2020.103426.
Texto completo da fonteWu, Wei, e Xuemeng Zhai. "DyLFG: A Dynamic Network Learning Framework Based on Geometry". Entropy 25, n.º 12 (30 de novembro de 2023): 1611. http://dx.doi.org/10.3390/e25121611.
Texto completo da fonteHuang, Yicong, e Zhuliang Yu. "Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models". Entropy 24, n.º 2 (19 de janeiro de 2022): 152. http://dx.doi.org/10.3390/e24020152.
Texto completo da fonteChristensen, Andrew J., Ananya Sen Gupta e Ivars Kirsteins. "Graph representation learning on braid manifolds". Journal of the Acoustical Society of America 152, n.º 4 (outubro de 2022): A39. http://dx.doi.org/10.1121/10.0015466.
Texto completo da fonteCadieu, Charles F., e Bruno A. Olshausen. "Learning Intermediate-Level Representations of Form and Motion from Natural Movies". Neural Computation 24, n.º 4 (abril de 2012): 827–66. http://dx.doi.org/10.1162/neco_a_00247.
Texto completo da fonteSun, Li, Zhongbao Zhang, Jiawei Zhang, Feiyang Wang, Hao Peng, Sen Su e Philip S. Yu. "Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 5 (18 de maio de 2021): 4375–83. http://dx.doi.org/10.1609/aaai.v35i5.16563.
Texto completo da fonteZheng, Tingyi, Yilin Zhang e Yuhang Wang. "Dynamic guided metric representation learning for multi-view clustering". PeerJ Computer Science 8 (8 de março de 2022): e922. http://dx.doi.org/10.7717/peerj-cs.922.
Texto completo da fonteLjubešić, Nikola. "‟Deep lexicography” – Fad or Opportunity?" Rasprave Instituta za hrvatski jezik i jezikoslovlje 46, n.º 2 (30 de outubro de 2020): 839–52. http://dx.doi.org/10.31724/rihjj.46.2.21.
Texto completo da fonteLi, Bin, Yunlong Fan, Miao Gao, Yikemaiti Sataer e Zhiqiang Gao. "A Joint-Learning-Based Dynamic Graph Learning Framework for Structured Prediction". Electronics 12, n.º 11 (23 de maio de 2023): 2357. http://dx.doi.org/10.3390/electronics12112357.
Texto completo da fonteGeng, Shijie, Peng Gao, Moitreya Chatterjee, Chiori Hori, Jonathan Le Roux, Yongfeng Zhang, Hongsheng Li e Anoop Cherian. "Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 2 (18 de maio de 2021): 1415–23. http://dx.doi.org/10.1609/aaai.v35i2.16231.
Texto completo da fonteVelasquez, Alvaro, Brett Bissey, Lior Barak, Daniel Melcer, Andre Beckus, Ismail Alkhouri e George Atia. "Multi-Agent Tree Search with Dynamic Reward Shaping". Proceedings of the International Conference on Automated Planning and Scheduling 32 (13 de junho de 2022): 652–61. http://dx.doi.org/10.1609/icaps.v32i1.19854.
Texto completo da fonteRen, Xiaobin, Kaiqi Zhao, Patricia J. Riddle, Katerina Taskova, Qingyi Pan e Lianyan Li. "DAMR: Dynamic Adjacency Matrix Representation Learning for Multivariate Time Series Imputation". Proceedings of the ACM on Management of Data 1, n.º 2 (13 de junho de 2023): 1–25. http://dx.doi.org/10.1145/3589333.
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 fontePerlovsky, Leonid, e Gary Kuvich. "Machine Learning and Cognitive Algorithms for Engineering Applications". International Journal of Cognitive Informatics and Natural Intelligence 7, n.º 4 (outubro de 2013): 64–82. http://dx.doi.org/10.4018/ijcini.2013100104.
Texto completo da fonteGeng, Yu, Zongbo Han, Changqing Zhang e Qinghua Hu. "Uncertainty-Aware Multi-View Representation Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 9 (18 de maio de 2021): 7545–53. http://dx.doi.org/10.1609/aaai.v35i9.16924.
Texto completo da fonteMalloy, Tyler, Yinuo Du, Fei Fang e Cleotilde Gonzalez. "Generative Environment-Representation Instance-Based Learning: A Cognitive Model". Proceedings of the AAAI Symposium Series 2, n.º 1 (22 de janeiro de 2024): 326–33. http://dx.doi.org/10.1609/aaaiss.v2i1.27696.
Texto completo da fonteLv, Feiya, Chenglin Wen e Meiqin Liu. "Dynamic reconstruction based representation learning for multivariable process monitoring". Journal of Process Control 81 (setembro de 2019): 112–25. http://dx.doi.org/10.1016/j.jprocont.2019.06.012.
Texto completo da fonteYin, Ying, Li-Xin Ji, Jian-Peng Zhang e Yu-Long Pei. "DHNE: Network Representation Learning Method for Dynamic Heterogeneous Networks". IEEE Access 7 (2019): 134782–92. http://dx.doi.org/10.1109/access.2019.2942221.
Texto completo da fonteZhang, Xiaoxian, Jianpei Zhang e Jing Yang. "Large-scale dynamic social data representation for structure feature learning". Journal of Intelligent & Fuzzy Systems 39, n.º 4 (21 de outubro de 2020): 5253–62. http://dx.doi.org/10.3233/jifs-189010.
Texto completo da fonteNajafi, Bahareh, Saeedeh Parsaeefard e Alberto Leon-Garcia. "Entropy-Aware Time-Varying Graph Neural Networks with Generalized Temporal Hawkes Process: Dynamic Link Prediction in the Presence of Node Addition and Deletion". Machine Learning and Knowledge Extraction 5, n.º 4 (4 de outubro de 2023): 1359–81. http://dx.doi.org/10.3390/make5040069.
Texto completo da fonteLai, Songxuan, Lianwen Jin, Luojun Lin, Yecheng Zhu e Huiyun Mao. "SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-World Verification". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 01 (3 de abril de 2020): 735–42. http://dx.doi.org/10.1609/aaai.v34i01.5416.
Texto completo da fonteLiu, Hao, Jindong Han, Yanjie Fu, Jingbo Zhou, Xinjiang Lu e Hui Xiong. "Multi-modal transportation recommendation with unified route representation learning". Proceedings of the VLDB Endowment 14, n.º 3 (novembro de 2020): 342–50. http://dx.doi.org/10.14778/3430915.3430924.
Texto completo da fonteJiang, Linxing Preston, e Rajesh P. N. Rao. "Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex". PLOS Computational Biology 20, n.º 2 (8 de fevereiro de 2024): e1011801. http://dx.doi.org/10.1371/journal.pcbi.1011801.
Texto completo da fonteHuang, Ru, Zijian Chen, Jianhua He e Xiaoli Chu. "Dynamic Heterogeneous User Generated Contents-Driven Relation Assessment via Graph Representation Learning". Sensors 22, n.º 4 (11 de fevereiro de 2022): 1402. http://dx.doi.org/10.3390/s22041402.
Texto completo da fonteFang, Yang, Xiang Zhao, Peixin Huang, Weidong Xiao e Maarten de Rijke. "Scalable Representation Learning for Dynamic Heterogeneous Information Networks via Metagraphs". ACM Transactions on Information Systems 40, n.º 4 (31 de outubro de 2022): 1–27. http://dx.doi.org/10.1145/3485189.
Texto completo da fonteThreja Malhotra, Ashu, e Jasneet Kaur. "Exploring the Role of Technological Representations to Facilitate Mathematics Learning In E-Class". International Journal of Multidisciplinary Research Configuration 1, n.º 3 (julho de 2021): 01–05. http://dx.doi.org/10.52984/ijomrc1301.
Texto completo da fonteFeng, Pengbin, Jianfeng Ma, Teng Li, Xindi Ma, Ning Xi e Di Lu. "Android Malware Detection via Graph Representation Learning". Mobile Information Systems 2021 (4 de junho de 2021): 1–14. http://dx.doi.org/10.1155/2021/5538841.
Texto completo da fonteFu, Sichao, Weifeng Liu, Weili Guan, Yicong Zhou, Dapeng Tao e Changsheng Xu. "Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification". ACM Transactions on Multimedia Computing, Communications, and Applications 17, n.º 1s (31 de março de 2021): 1–13. http://dx.doi.org/10.1145/3412846.
Texto completo da fonteXiang, Xintao, Tiancheng Huang e Donglin Wang. "Learning to Evolve on Dynamic Graphs (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junho de 2022): 13091–92. http://dx.doi.org/10.1609/aaai.v36i11.21682.
Texto completo da fonteHuang, Zhenhua, Zhenyu Wang e Rui Zhang. "Cascade2vec: Learning Dynamic Cascade Representation by Recurrent Graph Neural Networks". IEEE Access 7 (2019): 144800–144812. http://dx.doi.org/10.1109/access.2019.2942853.
Texto completo da fontePan, Jianguo, Huan Li, Jiajun Teng, Qin Zhao e Maozhen Li. "Dynamic Network Representation Learning Method Based on Improved GRU Network". Computing and Informatics 41, n.º 6 (2022): 1491–509. http://dx.doi.org/10.31577/cai_2022_6_1491.
Texto completo da fonteolde Scheper, Tjeerd V. "Criticality Analysis: Bio-Inspired Nonlinear Data Representation". Entropy 25, n.º 12 (14 de dezembro de 2023): 1660. http://dx.doi.org/10.3390/e25121660.
Texto completo da fonteZhu, Yingjie, Gregory Nachtrab, Piper C. Keyes, William E. Allen, Liqun Luo e Xiaoke Chen. "Dynamic salience processing in paraventricular thalamus gates associative learning". Science 362, n.º 6413 (25 de outubro de 2018): 423–29. http://dx.doi.org/10.1126/science.aat0481.
Texto completo da fonteWang, Lu, Georgia Hodges e Juyeon Lee. "Connecting Macroscopic, Molecular, and Symbolic Representations with Immersive Technologies in High School Chemistry: The Case of Redox Reactions". Education Sciences 12, n.º 7 (22 de junho de 2022): 428. http://dx.doi.org/10.3390/educsci12070428.
Texto completo da fonteCai, Yuanying, Chuheng Zhang, Wei Shen, Xuyun Zhang, Wenjie Ruan e Longbo Huang. "RePreM: Representation Pre-training with Masked Model for Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junho de 2023): 6879–87. http://dx.doi.org/10.1609/aaai.v37i6.25842.
Texto completo da fonteBeng Lee, Chwee, Keck Voon Ling, Peter Reimann, Yudho Ahmad Diponegoro, Chia Heng Koh e Derwin Chew. "Dynamic scaffolding in a cloud-based problem representation system". Campus-Wide Information Systems 31, n.º 5 (28 de outubro de 2014): 346–56. http://dx.doi.org/10.1108/cwis-02-2014-0006.
Texto completo da fonteSun, Zheng, Shad A. Torrie, Andrew W. Sumsion e Dah-Jye Lee. "Self-Supervised Facial Motion Representation Learning via Contrastive Subclips". Electronics 12, n.º 6 (13 de março de 2023): 1369. http://dx.doi.org/10.3390/electronics12061369.
Texto completo da fonteSchoeneman, Frank, Varun Chandola, Nils Napp, Olga Wodo e Jaroslaw Zola. "Learning Manifolds from Dynamic Process Data". Algorithms 13, n.º 2 (21 de janeiro de 2020): 30. http://dx.doi.org/10.3390/a13020030.
Texto completo da fonteHaga, Takeshi, Hiroshi Kera e Kazuhiko Kawamoto. "Sequential Variational Autoencoder with Adversarial Classifier for Video Disentanglement". Sensors 23, n.º 5 (24 de fevereiro de 2023): 2515. http://dx.doi.org/10.3390/s23052515.
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