Artykuły w czasopismach na temat „Dynamic Representation Learning”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Sprawdź 50 najlepszych artykułów w czasopismach naukowych na temat „Dynamic Representation Learning”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Przeglądaj artykuły w czasopismach z różnych dziedzin i twórz odpowiednie bibliografie.
Lee, Jungmin, i Wongyoung Lee. "Aspects of A Study on the Multi Presentational Metaphor Education Using Online Telestration". Korean Society of Culture and Convergence 44, nr 9 (30.09.2022): 163–73. http://dx.doi.org/10.33645/cnc.2022.9.44.9.163.
Pełny tekst źródłaBiswal, Siddharth, Cao Xiao, Lucas M. Glass, Elizabeth Milkovits i Jimeng Sun. "Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 01 (3.04.2020): 557–64. http://dx.doi.org/10.1609/aaai.v34i01.5394.
Pełny tekst źródłaWang, Xingqi, Mengrui Zhang, Bin Chen, Dan Wei i Yanli Shao. "Dynamic Weighted Multitask Learning and Contrastive Learning for Multimodal Sentiment Analysis". Electronics 12, nr 13 (7.07.2023): 2986. http://dx.doi.org/10.3390/electronics12132986.
Pełny tekst źródłaGoyal, Palash, Sujit Rokka Chhetri i Arquimedes Canedo. "dyngraph2vec: Capturing network dynamics using dynamic graph representation learning". Knowledge-Based Systems 187 (styczeń 2020): 104816. http://dx.doi.org/10.1016/j.knosys.2019.06.024.
Pełny tekst źródłaHan, Liangzhe, Ruixing Zhang, Leilei Sun, Bowen Du, Yanjie Fu i Tongyu Zhu. "Generic and Dynamic Graph Representation Learning for Crowd Flow Modeling". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 4 (26.06.2023): 4293–301. http://dx.doi.org/10.1609/aaai.v37i4.25548.
Pełny tekst źródłaJiao, Pengfei, Hongjiang Chen, Huijun Tang, Qing Bao, Long Zhang, Zhidong Zhao i Huaming Wu. "Contrastive representation learning on dynamic networks". Neural Networks 174 (czerwiec 2024): 106240. http://dx.doi.org/10.1016/j.neunet.2024.106240.
Pełny tekst źródłaRadulescu, Angela, Yeon Soon Shin i Yael Niv. "Human Representation Learning". Annual Review of Neuroscience 44, nr 1 (8.07.2021): 253–73. http://dx.doi.org/10.1146/annurev-neuro-092920-120559.
Pełny tekst źródłaLiu, Dianbo, Alex Lamb, Xu Ji, Pascal Junior Tikeng Notsawo, Michael Mozer, Yoshua Bengio i Kenji Kawaguchi. "Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 7 (26.06.2023): 8825–33. http://dx.doi.org/10.1609/aaai.v37i7.26061.
Pełny tekst źródłaDeng, Yongjian, Hao Chen i Youfu Li. "A Dynamic GCN with Cross-Representation Distillation for Event-Based Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 2 (24.03.2024): 1492–500. http://dx.doi.org/10.1609/aaai.v38i2.27914.
Pełny tekst źródłaLi, Jintang, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu i Changhua Meng. "Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 7 (26.06.2023): 8588–96. http://dx.doi.org/10.1609/aaai.v37i7.26034.
Pełny tekst źródłaWei, Hao, Guyu Hu, Wei Bai, Shiming Xia i Zhisong Pan. "Lifelong representation learning in dynamic attributed networks". Neurocomputing 358 (wrzesień 2019): 1–9. http://dx.doi.org/10.1016/j.neucom.2019.05.038.
Pełny tekst źródłaLee, Dongha, Xiaoqian Jiang i Hwanjo Yu. "Harmonized representation learning on dynamic EHR graphs". Journal of Biomedical Informatics 106 (czerwiec 2020): 103426. http://dx.doi.org/10.1016/j.jbi.2020.103426.
Pełny tekst źródłaWu, Wei, i Xuemeng Zhai. "DyLFG: A Dynamic Network Learning Framework Based on Geometry". Entropy 25, nr 12 (30.11.2023): 1611. http://dx.doi.org/10.3390/e25121611.
Pełny tekst źródłaHuang, Yicong, i Zhuliang Yu. "Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models". Entropy 24, nr 2 (19.01.2022): 152. http://dx.doi.org/10.3390/e24020152.
Pełny tekst źródłaChristensen, Andrew J., Ananya Sen Gupta i Ivars Kirsteins. "Graph representation learning on braid manifolds". Journal of the Acoustical Society of America 152, nr 4 (październik 2022): A39. http://dx.doi.org/10.1121/10.0015466.
Pełny tekst źródłaCadieu, Charles F., i Bruno A. Olshausen. "Learning Intermediate-Level Representations of Form and Motion from Natural Movies". Neural Computation 24, nr 4 (kwiecień 2012): 827–66. http://dx.doi.org/10.1162/neco_a_00247.
Pełny tekst źródłaSun, Li, Zhongbao Zhang, Jiawei Zhang, Feiyang Wang, Hao Peng, Sen Su i Philip S. Yu. "Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 5 (18.05.2021): 4375–83. http://dx.doi.org/10.1609/aaai.v35i5.16563.
Pełny tekst źródłaZheng, Tingyi, Yilin Zhang i Yuhang Wang. "Dynamic guided metric representation learning for multi-view clustering". PeerJ Computer Science 8 (8.03.2022): e922. http://dx.doi.org/10.7717/peerj-cs.922.
Pełny tekst źródłaLjubešić, Nikola. "‟Deep lexicography” – Fad or Opportunity?" Rasprave Instituta za hrvatski jezik i jezikoslovlje 46, nr 2 (30.10.2020): 839–52. http://dx.doi.org/10.31724/rihjj.46.2.21.
Pełny tekst źródłaLi, Bin, Yunlong Fan, Miao Gao, Yikemaiti Sataer i Zhiqiang Gao. "A Joint-Learning-Based Dynamic Graph Learning Framework for Structured Prediction". Electronics 12, nr 11 (23.05.2023): 2357. http://dx.doi.org/10.3390/electronics12112357.
Pełny tekst źródłaGeng, Shijie, Peng Gao, Moitreya Chatterjee, Chiori Hori, Jonathan Le Roux, Yongfeng Zhang, Hongsheng Li i Anoop Cherian. "Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 2 (18.05.2021): 1415–23. http://dx.doi.org/10.1609/aaai.v35i2.16231.
Pełny tekst źródłaVelasquez, Alvaro, Brett Bissey, Lior Barak, Daniel Melcer, Andre Beckus, Ismail Alkhouri i George Atia. "Multi-Agent Tree Search with Dynamic Reward Shaping". Proceedings of the International Conference on Automated Planning and Scheduling 32 (13.06.2022): 652–61. http://dx.doi.org/10.1609/icaps.v32i1.19854.
Pełny tekst źródłaRen, Xiaobin, Kaiqi Zhao, Patricia J. Riddle, Katerina Taskova, Qingyi Pan i Lianyan Li. "DAMR: Dynamic Adjacency Matrix Representation Learning for Multivariate Time Series Imputation". Proceedings of the ACM on Management of Data 1, nr 2 (13.06.2023): 1–25. http://dx.doi.org/10.1145/3589333.
Pełny tekst źródłaAchille, Alessandro, i 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.
Pełny tekst źródłaPerlovsky, Leonid, i Gary Kuvich. "Machine Learning and Cognitive Algorithms for Engineering Applications". International Journal of Cognitive Informatics and Natural Intelligence 7, nr 4 (październik 2013): 64–82. http://dx.doi.org/10.4018/ijcini.2013100104.
Pełny tekst źródłaGeng, Yu, Zongbo Han, Changqing Zhang i Qinghua Hu. "Uncertainty-Aware Multi-View Representation Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 9 (18.05.2021): 7545–53. http://dx.doi.org/10.1609/aaai.v35i9.16924.
Pełny tekst źródłaMalloy, Tyler, Yinuo Du, Fei Fang i Cleotilde Gonzalez. "Generative Environment-Representation Instance-Based Learning: A Cognitive Model". Proceedings of the AAAI Symposium Series 2, nr 1 (22.01.2024): 326–33. http://dx.doi.org/10.1609/aaaiss.v2i1.27696.
Pełny tekst źródłaLv, Feiya, Chenglin Wen i Meiqin Liu. "Dynamic reconstruction based representation learning for multivariable process monitoring". Journal of Process Control 81 (wrzesień 2019): 112–25. http://dx.doi.org/10.1016/j.jprocont.2019.06.012.
Pełny tekst źródłaYin, Ying, Li-Xin Ji, Jian-Peng Zhang i 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.
Pełny tekst źródłaZhang, Xiaoxian, Jianpei Zhang i Jing Yang. "Large-scale dynamic social data representation for structure feature learning". Journal of Intelligent & Fuzzy Systems 39, nr 4 (21.10.2020): 5253–62. http://dx.doi.org/10.3233/jifs-189010.
Pełny tekst źródłaNajafi, Bahareh, Saeedeh Parsaeefard i 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, nr 4 (4.10.2023): 1359–81. http://dx.doi.org/10.3390/make5040069.
Pełny tekst źródłaLai, Songxuan, Lianwen Jin, Luojun Lin, Yecheng Zhu i Huiyun Mao. "SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-World Verification". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 01 (3.04.2020): 735–42. http://dx.doi.org/10.1609/aaai.v34i01.5416.
Pełny tekst źródłaLiu, Hao, Jindong Han, Yanjie Fu, Jingbo Zhou, Xinjiang Lu i Hui Xiong. "Multi-modal transportation recommendation with unified route representation learning". Proceedings of the VLDB Endowment 14, nr 3 (listopad 2020): 342–50. http://dx.doi.org/10.14778/3430915.3430924.
Pełny tekst źródłaJiang, Linxing Preston, i Rajesh P. N. Rao. "Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex". PLOS Computational Biology 20, nr 2 (8.02.2024): e1011801. http://dx.doi.org/10.1371/journal.pcbi.1011801.
Pełny tekst źródłaHuang, Ru, Zijian Chen, Jianhua He i Xiaoli Chu. "Dynamic Heterogeneous User Generated Contents-Driven Relation Assessment via Graph Representation Learning". Sensors 22, nr 4 (11.02.2022): 1402. http://dx.doi.org/10.3390/s22041402.
Pełny tekst źródłaFang, Yang, Xiang Zhao, Peixin Huang, Weidong Xiao i Maarten de Rijke. "Scalable Representation Learning for Dynamic Heterogeneous Information Networks via Metagraphs". ACM Transactions on Information Systems 40, nr 4 (31.10.2022): 1–27. http://dx.doi.org/10.1145/3485189.
Pełny tekst źródłaThreja Malhotra, Ashu, i Jasneet Kaur. "Exploring the Role of Technological Representations to Facilitate Mathematics Learning In E-Class". International Journal of Multidisciplinary Research Configuration 1, nr 3 (lipiec 2021): 01–05. http://dx.doi.org/10.52984/ijomrc1301.
Pełny tekst źródłaFeng, Pengbin, Jianfeng Ma, Teng Li, Xindi Ma, Ning Xi i Di Lu. "Android Malware Detection via Graph Representation Learning". Mobile Information Systems 2021 (4.06.2021): 1–14. http://dx.doi.org/10.1155/2021/5538841.
Pełny tekst źródłaFu, Sichao, Weifeng Liu, Weili Guan, Yicong Zhou, Dapeng Tao i Changsheng Xu. "Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification". ACM Transactions on Multimedia Computing, Communications, and Applications 17, nr 1s (31.03.2021): 1–13. http://dx.doi.org/10.1145/3412846.
Pełny tekst źródłaXiang, Xintao, Tiancheng Huang i Donglin Wang. "Learning to Evolve on Dynamic Graphs (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 11 (28.06.2022): 13091–92. http://dx.doi.org/10.1609/aaai.v36i11.21682.
Pełny tekst źródłaHuang, Zhenhua, Zhenyu Wang i 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.
Pełny tekst źródłaPan, Jianguo, Huan Li, Jiajun Teng, Qin Zhao i Maozhen Li. "Dynamic Network Representation Learning Method Based on Improved GRU Network". Computing and Informatics 41, nr 6 (2022): 1491–509. http://dx.doi.org/10.31577/cai_2022_6_1491.
Pełny tekst źródłaolde Scheper, Tjeerd V. "Criticality Analysis: Bio-Inspired Nonlinear Data Representation". Entropy 25, nr 12 (14.12.2023): 1660. http://dx.doi.org/10.3390/e25121660.
Pełny tekst źródłaZhu, Yingjie, Gregory Nachtrab, Piper C. Keyes, William E. Allen, Liqun Luo i Xiaoke Chen. "Dynamic salience processing in paraventricular thalamus gates associative learning". Science 362, nr 6413 (25.10.2018): 423–29. http://dx.doi.org/10.1126/science.aat0481.
Pełny tekst źródłaWang, Lu, Georgia Hodges i Juyeon Lee. "Connecting Macroscopic, Molecular, and Symbolic Representations with Immersive Technologies in High School Chemistry: The Case of Redox Reactions". Education Sciences 12, nr 7 (22.06.2022): 428. http://dx.doi.org/10.3390/educsci12070428.
Pełny tekst źródłaCai, Yuanying, Chuheng Zhang, Wei Shen, Xuyun Zhang, Wenjie Ruan i Longbo Huang. "RePreM: Representation Pre-training with Masked Model for Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 6 (26.06.2023): 6879–87. http://dx.doi.org/10.1609/aaai.v37i6.25842.
Pełny tekst źródłaBeng Lee, Chwee, Keck Voon Ling, Peter Reimann, Yudho Ahmad Diponegoro, Chia Heng Koh i Derwin Chew. "Dynamic scaffolding in a cloud-based problem representation system". Campus-Wide Information Systems 31, nr 5 (28.10.2014): 346–56. http://dx.doi.org/10.1108/cwis-02-2014-0006.
Pełny tekst źródłaSun, Zheng, Shad A. Torrie, Andrew W. Sumsion i Dah-Jye Lee. "Self-Supervised Facial Motion Representation Learning via Contrastive Subclips". Electronics 12, nr 6 (13.03.2023): 1369. http://dx.doi.org/10.3390/electronics12061369.
Pełny tekst źródłaSchoeneman, Frank, Varun Chandola, Nils Napp, Olga Wodo i Jaroslaw Zola. "Learning Manifolds from Dynamic Process Data". Algorithms 13, nr 2 (21.01.2020): 30. http://dx.doi.org/10.3390/a13020030.
Pełny tekst źródłaHaga, Takeshi, Hiroshi Kera i Kazuhiko Kawamoto. "Sequential Variational Autoencoder with Adversarial Classifier for Video Disentanglement". Sensors 23, nr 5 (24.02.2023): 2515. http://dx.doi.org/10.3390/s23052515.
Pełny tekst źródła