Journal articles on the topic 'Dynamic Representation Learning'
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Lee, Jungmin, and Wongyoung Lee. "Aspects of A Study on the Multi Presentational Metaphor Education Using Online Telestration." Korean Society of Culture and Convergence 44, no. 9 (September 30, 2022): 163–73. http://dx.doi.org/10.33645/cnc.2022.9.44.9.163.
Biswal, Siddharth, Cao Xiao, Lucas M. Glass, Elizabeth Milkovits, and Jimeng Sun. "Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 557–64. http://dx.doi.org/10.1609/aaai.v34i01.5394.
Wang, Xingqi, Mengrui Zhang, Bin Chen, Dan Wei, and Yanli Shao. "Dynamic Weighted Multitask Learning and Contrastive Learning for Multimodal Sentiment Analysis." Electronics 12, no. 13 (July 7, 2023): 2986. http://dx.doi.org/10.3390/electronics12132986.
Goyal, Palash, Sujit Rokka Chhetri, and Arquimedes Canedo. "dyngraph2vec: Capturing network dynamics using dynamic graph representation learning." Knowledge-Based Systems 187 (January 2020): 104816. http://dx.doi.org/10.1016/j.knosys.2019.06.024.
Han, Liangzhe, Ruixing Zhang, Leilei Sun, Bowen Du, Yanjie Fu, and Tongyu Zhu. "Generic and Dynamic Graph Representation Learning for Crowd Flow Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4293–301. http://dx.doi.org/10.1609/aaai.v37i4.25548.
Jiao, Pengfei, Hongjiang Chen, Huijun Tang, Qing Bao, Long Zhang, Zhidong Zhao, and Huaming Wu. "Contrastive representation learning on dynamic networks." Neural Networks 174 (June 2024): 106240. http://dx.doi.org/10.1016/j.neunet.2024.106240.
Radulescu, Angela, Yeon Soon Shin, and Yael Niv. "Human Representation Learning." Annual Review of Neuroscience 44, no. 1 (July 8, 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, and Kenji Kawaguchi. "Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8825–33. http://dx.doi.org/10.1609/aaai.v37i7.26061.
Deng, Yongjian, Hao Chen, and Youfu Li. "A Dynamic GCN with Cross-Representation Distillation for Event-Based Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 2 (March 24, 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, and Changhua Meng. "Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8588–96. http://dx.doi.org/10.1609/aaai.v37i7.26034.
Wei, Hao, Guyu Hu, Wei Bai, Shiming Xia, and Zhisong Pan. "Lifelong representation learning in dynamic attributed networks." Neurocomputing 358 (September 2019): 1–9. http://dx.doi.org/10.1016/j.neucom.2019.05.038.
Lee, Dongha, Xiaoqian Jiang, and Hwanjo Yu. "Harmonized representation learning on dynamic EHR graphs." Journal of Biomedical Informatics 106 (June 2020): 103426. http://dx.doi.org/10.1016/j.jbi.2020.103426.
Wu, Wei, and Xuemeng Zhai. "DyLFG: A Dynamic Network Learning Framework Based on Geometry." Entropy 25, no. 12 (November 30, 2023): 1611. http://dx.doi.org/10.3390/e25121611.
Huang, Yicong, and Zhuliang Yu. "Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models." Entropy 24, no. 2 (January 19, 2022): 152. http://dx.doi.org/10.3390/e24020152.
Christensen, Andrew J., Ananya Sen Gupta, and Ivars Kirsteins. "Graph representation learning on braid manifolds." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A39. http://dx.doi.org/10.1121/10.0015466.
Cadieu, Charles F., and Bruno A. Olshausen. "Learning Intermediate-Level Representations of Form and Motion from Natural Movies." Neural Computation 24, no. 4 (April 2012): 827–66. http://dx.doi.org/10.1162/neco_a_00247.
Sun, Li, Zhongbao Zhang, Jiawei Zhang, Feiyang Wang, Hao Peng, Sen Su, and Philip S. Yu. "Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4375–83. http://dx.doi.org/10.1609/aaai.v35i5.16563.
Zheng, Tingyi, Yilin Zhang, and Yuhang Wang. "Dynamic guided metric representation learning for multi-view clustering." PeerJ Computer Science 8 (March 8, 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, no. 2 (October 30, 2020): 839–52. http://dx.doi.org/10.31724/rihjj.46.2.21.
Li, Bin, Yunlong Fan, Miao Gao, Yikemaiti Sataer, and Zhiqiang Gao. "A Joint-Learning-Based Dynamic Graph Learning Framework for Structured Prediction." Electronics 12, no. 11 (May 23, 2023): 2357. http://dx.doi.org/10.3390/electronics12112357.
Geng, Shijie, Peng Gao, Moitreya Chatterjee, Chiori Hori, Jonathan Le Roux, Yongfeng Zhang, Hongsheng Li, and Anoop Cherian. "Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1415–23. http://dx.doi.org/10.1609/aaai.v35i2.16231.
Velasquez, Alvaro, Brett Bissey, Lior Barak, Daniel Melcer, Andre Beckus, Ismail Alkhouri, and George Atia. "Multi-Agent Tree Search with Dynamic Reward Shaping." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 652–61. http://dx.doi.org/10.1609/icaps.v32i1.19854.
Ren, Xiaobin, Kaiqi Zhao, Patricia J. Riddle, Katerina Taskova, Qingyi Pan, and Lianyan Li. "DAMR: Dynamic Adjacency Matrix Representation Learning for Multivariate Time Series Imputation." Proceedings of the ACM on Management of Data 1, no. 2 (June 13, 2023): 1–25. http://dx.doi.org/10.1145/3589333.
Achille, Alessandro, and Stefano Soatto. "A Separation Principle for Control in the Age of Deep Learning." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 2018): 287–307. http://dx.doi.org/10.1146/annurev-control-060117-105140.
Perlovsky, Leonid, and Gary Kuvich. "Machine Learning and Cognitive Algorithms for Engineering Applications." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 4 (October 2013): 64–82. http://dx.doi.org/10.4018/ijcini.2013100104.
Geng, Yu, Zongbo Han, Changqing Zhang, and Qinghua Hu. "Uncertainty-Aware Multi-View Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7545–53. http://dx.doi.org/10.1609/aaai.v35i9.16924.
Malloy, Tyler, Yinuo Du, Fei Fang, and Cleotilde Gonzalez. "Generative Environment-Representation Instance-Based Learning: A Cognitive Model." Proceedings of the AAAI Symposium Series 2, no. 1 (January 22, 2024): 326–33. http://dx.doi.org/10.1609/aaaiss.v2i1.27696.
Lv, Feiya, Chenglin Wen, and Meiqin Liu. "Dynamic reconstruction based representation learning for multivariable process monitoring." Journal of Process Control 81 (September 2019): 112–25. http://dx.doi.org/10.1016/j.jprocont.2019.06.012.
Yin, Ying, Li-Xin Ji, Jian-Peng Zhang, and 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, and Jing Yang. "Large-scale dynamic social data representation for structure feature learning." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5253–62. http://dx.doi.org/10.3233/jifs-189010.
Najafi, Bahareh, Saeedeh Parsaeefard, and 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, no. 4 (October 4, 2023): 1359–81. http://dx.doi.org/10.3390/make5040069.
Lai, Songxuan, Lianwen Jin, Luojun Lin, Yecheng Zhu, and Huiyun Mao. "SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-World Verification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 735–42. http://dx.doi.org/10.1609/aaai.v34i01.5416.
Liu, Hao, Jindong Han, Yanjie Fu, Jingbo Zhou, Xinjiang Lu, and Hui Xiong. "Multi-modal transportation recommendation with unified route representation learning." Proceedings of the VLDB Endowment 14, no. 3 (November 2020): 342–50. http://dx.doi.org/10.14778/3430915.3430924.
Jiang, Linxing Preston, and Rajesh P. N. Rao. "Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex." PLOS Computational Biology 20, no. 2 (February 8, 2024): e1011801. http://dx.doi.org/10.1371/journal.pcbi.1011801.
Huang, Ru, Zijian Chen, Jianhua He, and Xiaoli Chu. "Dynamic Heterogeneous User Generated Contents-Driven Relation Assessment via Graph Representation Learning." Sensors 22, no. 4 (February 11, 2022): 1402. http://dx.doi.org/10.3390/s22041402.
Fang, Yang, Xiang Zhao, Peixin Huang, Weidong Xiao, and Maarten de Rijke. "Scalable Representation Learning for Dynamic Heterogeneous Information Networks via Metagraphs." ACM Transactions on Information Systems 40, no. 4 (October 31, 2022): 1–27. http://dx.doi.org/10.1145/3485189.
Threja Malhotra, Ashu, and Jasneet Kaur. "Exploring the Role of Technological Representations to Facilitate Mathematics Learning In E-Class." International Journal of Multidisciplinary Research Configuration 1, no. 3 (July 2021): 01–05. http://dx.doi.org/10.52984/ijomrc1301.
Feng, Pengbin, Jianfeng Ma, Teng Li, Xindi Ma, Ning Xi, and Di Lu. "Android Malware Detection via Graph Representation Learning." Mobile Information Systems 2021 (June 4, 2021): 1–14. http://dx.doi.org/10.1155/2021/5538841.
Fu, Sichao, Weifeng Liu, Weili Guan, Yicong Zhou, Dapeng Tao, and Changsheng Xu. "Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (March 31, 2021): 1–13. http://dx.doi.org/10.1145/3412846.
Xiang, Xintao, Tiancheng Huang, and Donglin Wang. "Learning to Evolve on Dynamic Graphs (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13091–92. http://dx.doi.org/10.1609/aaai.v36i11.21682.
Huang, Zhenhua, Zhenyu Wang, and 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, and Maozhen Li. "Dynamic Network Representation Learning Method Based on Improved GRU Network." Computing and Informatics 41, no. 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, no. 12 (December 14, 2023): 1660. http://dx.doi.org/10.3390/e25121660.
Zhu, Yingjie, Gregory Nachtrab, Piper C. Keyes, William E. Allen, Liqun Luo, and Xiaoke Chen. "Dynamic salience processing in paraventricular thalamus gates associative learning." Science 362, no. 6413 (October 25, 2018): 423–29. http://dx.doi.org/10.1126/science.aat0481.
Wang, Lu, Georgia Hodges, and Juyeon Lee. "Connecting Macroscopic, Molecular, and Symbolic Representations with Immersive Technologies in High School Chemistry: The Case of Redox Reactions." Education Sciences 12, no. 7 (June 22, 2022): 428. http://dx.doi.org/10.3390/educsci12070428.
Cai, 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.
Beng Lee, Chwee, Keck Voon Ling, Peter Reimann, Yudho Ahmad Diponegoro, Chia Heng Koh, and Derwin Chew. "Dynamic scaffolding in a cloud-based problem representation system." Campus-Wide Information Systems 31, no. 5 (October 28, 2014): 346–56. http://dx.doi.org/10.1108/cwis-02-2014-0006.
Sun, Zheng, Shad A. Torrie, Andrew W. Sumsion, and Dah-Jye Lee. "Self-Supervised Facial Motion Representation Learning via Contrastive Subclips." Electronics 12, no. 6 (March 13, 2023): 1369. http://dx.doi.org/10.3390/electronics12061369.
Schoeneman, Frank, Varun Chandola, Nils Napp, Olga Wodo, and Jaroslaw Zola. "Learning Manifolds from Dynamic Process Data." Algorithms 13, no. 2 (January 21, 2020): 30. http://dx.doi.org/10.3390/a13020030.
Haga, Takeshi, Hiroshi Kera, and Kazuhiko Kawamoto. "Sequential Variational Autoencoder with Adversarial Classifier for Video Disentanglement." Sensors 23, no. 5 (February 24, 2023): 2515. http://dx.doi.org/10.3390/s23052515.