Artículos de revistas sobre el tema "Dynamic Representation Learning"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte los 50 mejores artículos de revistas para su investigación sobre el tema "Dynamic Representation Learning".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Explore artículos de revistas sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.
Lee, Jungmin y 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 septiembre de 2022): 163–73. http://dx.doi.org/10.33645/cnc.2022.9.44.9.163.
Biswal, Siddharth, Cao Xiao, Lucas M. Glass, Elizabeth Milkovits y 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.
Wang, Xingqi, Mengrui Zhang, Bin Chen, Dan Wei y Yanli Shao. "Dynamic Weighted Multitask Learning and Contrastive Learning for Multimodal Sentiment Analysis". Electronics 12, n.º 13 (7 de julio de 2023): 2986. http://dx.doi.org/10.3390/electronics12132986.
Goyal, Palash, Sujit Rokka Chhetri y Arquimedes Canedo. "dyngraph2vec: Capturing network dynamics using dynamic graph representation learning". Knowledge-Based Systems 187 (enero de 2020): 104816. http://dx.doi.org/10.1016/j.knosys.2019.06.024.
Han, Liangzhe, Ruixing Zhang, Leilei Sun, Bowen Du, Yanjie Fu y 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 junio de 2023): 4293–301. http://dx.doi.org/10.1609/aaai.v37i4.25548.
Jiao, Pengfei, Hongjiang Chen, Huijun Tang, Qing Bao, Long Zhang, Zhidong Zhao y Huaming Wu. "Contrastive representation learning on dynamic networks". Neural Networks 174 (junio de 2024): 106240. http://dx.doi.org/10.1016/j.neunet.2024.106240.
Radulescu, Angela, Yeon Soon Shin y Yael Niv. "Human Representation Learning". Annual Review of Neuroscience 44, n.º 1 (8 de julio de 2021): 253–73. http://dx.doi.org/10.1146/annurev-neuro-092920-120559.
Liu, Dianbo, Alex Lamb, Xu Ji, Pascal Junior Tikeng Notsawo, Michael Mozer, Yoshua Bengio y 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 junio de 2023): 8825–33. http://dx.doi.org/10.1609/aaai.v37i7.26061.
Deng, Yongjian, Hao Chen y 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 marzo de 2024): 1492–500. http://dx.doi.org/10.1609/aaai.v38i2.27914.
Li, Jintang, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu y 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 junio de 2023): 8588–96. http://dx.doi.org/10.1609/aaai.v37i7.26034.
Wei, Hao, Guyu Hu, Wei Bai, Shiming Xia y Zhisong Pan. "Lifelong representation learning in dynamic attributed networks". Neurocomputing 358 (septiembre de 2019): 1–9. http://dx.doi.org/10.1016/j.neucom.2019.05.038.
Lee, Dongha, Xiaoqian Jiang y Hwanjo Yu. "Harmonized representation learning on dynamic EHR graphs". Journal of Biomedical Informatics 106 (junio de 2020): 103426. http://dx.doi.org/10.1016/j.jbi.2020.103426.
Wu, Wei y Xuemeng Zhai. "DyLFG: A Dynamic Network Learning Framework Based on Geometry". Entropy 25, n.º 12 (30 de noviembre de 2023): 1611. http://dx.doi.org/10.3390/e25121611.
Huang, Yicong y Zhuliang Yu. "Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models". Entropy 24, n.º 2 (19 de enero de 2022): 152. http://dx.doi.org/10.3390/e24020152.
Christensen, Andrew J., Ananya Sen Gupta y Ivars Kirsteins. "Graph representation learning on braid manifolds". Journal of the Acoustical Society of America 152, n.º 4 (octubre de 2022): A39. http://dx.doi.org/10.1121/10.0015466.
Cadieu, Charles F. y 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.
Sun, Li, Zhongbao Zhang, Jiawei Zhang, Feiyang Wang, Hao Peng, Sen Su y 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 mayo de 2021): 4375–83. http://dx.doi.org/10.1609/aaai.v35i5.16563.
Zheng, Tingyi, Yilin Zhang y Yuhang Wang. "Dynamic guided metric representation learning for multi-view clustering". PeerJ Computer Science 8 (8 de marzo de 2022): e922. http://dx.doi.org/10.7717/peerj-cs.922.
Ljubešić, Nikola. "‟Deep lexicography” – Fad or Opportunity?" Rasprave Instituta za hrvatski jezik i jezikoslovlje 46, n.º 2 (30 de octubre de 2020): 839–52. http://dx.doi.org/10.31724/rihjj.46.2.21.
Li, Bin, Yunlong Fan, Miao Gao, Yikemaiti Sataer y Zhiqiang Gao. "A Joint-Learning-Based Dynamic Graph Learning Framework for Structured Prediction". Electronics 12, n.º 11 (23 de mayo de 2023): 2357. http://dx.doi.org/10.3390/electronics12112357.
Geng, Shijie, Peng Gao, Moitreya Chatterjee, Chiori Hori, Jonathan Le Roux, Yongfeng Zhang, Hongsheng Li y 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 mayo de 2021): 1415–23. http://dx.doi.org/10.1609/aaai.v35i2.16231.
Velasquez, Alvaro, Brett Bissey, Lior Barak, Daniel Melcer, Andre Beckus, Ismail Alkhouri y George Atia. "Multi-Agent Tree Search with Dynamic Reward Shaping". Proceedings of the International Conference on Automated Planning and Scheduling 32 (13 de junio de 2022): 652–61. http://dx.doi.org/10.1609/icaps.v32i1.19854.
Ren, Xiaobin, Kaiqi Zhao, Patricia J. Riddle, Katerina Taskova, Qingyi Pan y 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 junio de 2023): 1–25. http://dx.doi.org/10.1145/3589333.
Achille, Alessandro y 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 mayo de 2018): 287–307. http://dx.doi.org/10.1146/annurev-control-060117-105140.
Perlovsky, Leonid y Gary Kuvich. "Machine Learning and Cognitive Algorithms for Engineering Applications". International Journal of Cognitive Informatics and Natural Intelligence 7, n.º 4 (octubre de 2013): 64–82. http://dx.doi.org/10.4018/ijcini.2013100104.
Geng, Yu, Zongbo Han, Changqing Zhang y Qinghua Hu. "Uncertainty-Aware Multi-View Representation Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 9 (18 de mayo de 2021): 7545–53. http://dx.doi.org/10.1609/aaai.v35i9.16924.
Malloy, Tyler, Yinuo Du, Fei Fang y Cleotilde Gonzalez. "Generative Environment-Representation Instance-Based Learning: A Cognitive Model". Proceedings of the AAAI Symposium Series 2, n.º 1 (22 de enero de 2024): 326–33. http://dx.doi.org/10.1609/aaaiss.v2i1.27696.
Lv, Feiya, Chenglin Wen y Meiqin Liu. "Dynamic reconstruction based representation learning for multivariable process monitoring". Journal of Process Control 81 (septiembre de 2019): 112–25. http://dx.doi.org/10.1016/j.jprocont.2019.06.012.
Yin, Ying, Li-Xin Ji, Jian-Peng Zhang y 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.
Zhang, Xiaoxian, Jianpei Zhang y Jing Yang. "Large-scale dynamic social data representation for structure feature learning". Journal of Intelligent & Fuzzy Systems 39, n.º 4 (21 de octubre de 2020): 5253–62. http://dx.doi.org/10.3233/jifs-189010.
Najafi, Bahareh, Saeedeh Parsaeefard y 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 octubre de 2023): 1359–81. http://dx.doi.org/10.3390/make5040069.
Lai, Songxuan, Lianwen Jin, Luojun Lin, Yecheng Zhu y 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.
Liu, Hao, Jindong Han, Yanjie Fu, Jingbo Zhou, Xinjiang Lu y Hui Xiong. "Multi-modal transportation recommendation with unified route representation learning". Proceedings of the VLDB Endowment 14, n.º 3 (noviembre de 2020): 342–50. http://dx.doi.org/10.14778/3430915.3430924.
Jiang, Linxing Preston y 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 febrero de 2024): e1011801. http://dx.doi.org/10.1371/journal.pcbi.1011801.
Huang, Ru, Zijian Chen, Jianhua He y Xiaoli Chu. "Dynamic Heterogeneous User Generated Contents-Driven Relation Assessment via Graph Representation Learning". Sensors 22, n.º 4 (11 de febrero de 2022): 1402. http://dx.doi.org/10.3390/s22041402.
Fang, Yang, Xiang Zhao, Peixin Huang, Weidong Xiao y Maarten de Rijke. "Scalable Representation Learning for Dynamic Heterogeneous Information Networks via Metagraphs". ACM Transactions on Information Systems 40, n.º 4 (31 de octubre de 2022): 1–27. http://dx.doi.org/10.1145/3485189.
Threja Malhotra, Ashu y 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 (julio de 2021): 01–05. http://dx.doi.org/10.52984/ijomrc1301.
Feng, Pengbin, Jianfeng Ma, Teng Li, Xindi Ma, Ning Xi y Di Lu. "Android Malware Detection via Graph Representation Learning". Mobile Information Systems 2021 (4 de junio de 2021): 1–14. http://dx.doi.org/10.1155/2021/5538841.
Fu, Sichao, Weifeng Liu, Weili Guan, Yicong Zhou, Dapeng Tao y 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 marzo de 2021): 1–13. http://dx.doi.org/10.1145/3412846.
Xiang, Xintao, Tiancheng Huang y Donglin Wang. "Learning to Evolve on Dynamic Graphs (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junio de 2022): 13091–92. http://dx.doi.org/10.1609/aaai.v36i11.21682.
Huang, Zhenhua, Zhenyu Wang y 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.
Pan, Jianguo, Huan Li, Jiajun Teng, Qin Zhao y 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.
olde Scheper, Tjeerd V. "Criticality Analysis: Bio-Inspired Nonlinear Data Representation". Entropy 25, n.º 12 (14 de diciembre de 2023): 1660. http://dx.doi.org/10.3390/e25121660.
Zhu, Yingjie, Gregory Nachtrab, Piper C. Keyes, William E. Allen, Liqun Luo y Xiaoke Chen. "Dynamic salience processing in paraventricular thalamus gates associative learning". Science 362, n.º 6413 (25 de octubre de 2018): 423–29. http://dx.doi.org/10.1126/science.aat0481.
Wang, Lu, Georgia Hodges y 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 junio de 2022): 428. http://dx.doi.org/10.3390/educsci12070428.
Cai, Yuanying, Chuheng Zhang, Wei Shen, Xuyun Zhang, Wenjie Ruan y 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 junio de 2023): 6879–87. http://dx.doi.org/10.1609/aaai.v37i6.25842.
Beng Lee, Chwee, Keck Voon Ling, Peter Reimann, Yudho Ahmad Diponegoro, Chia Heng Koh y Derwin Chew. "Dynamic scaffolding in a cloud-based problem representation system". Campus-Wide Information Systems 31, n.º 5 (28 de octubre de 2014): 346–56. http://dx.doi.org/10.1108/cwis-02-2014-0006.
Sun, Zheng, Shad A. Torrie, Andrew W. Sumsion y Dah-Jye Lee. "Self-Supervised Facial Motion Representation Learning via Contrastive Subclips". Electronics 12, n.º 6 (13 de marzo de 2023): 1369. http://dx.doi.org/10.3390/electronics12061369.
Schoeneman, Frank, Varun Chandola, Nils Napp, Olga Wodo y Jaroslaw Zola. "Learning Manifolds from Dynamic Process Data". Algorithms 13, n.º 2 (21 de enero de 2020): 30. http://dx.doi.org/10.3390/a13020030.
Haga, Takeshi, Hiroshi Kera y Kazuhiko Kawamoto. "Sequential Variational Autoencoder with Adversarial Classifier for Video Disentanglement". Sensors 23, n.º 5 (24 de febrero de 2023): 2515. http://dx.doi.org/10.3390/s23052515.