Literatura académica sobre el tema "Hypergraph-structure"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Hypergraph-structure".
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.
Artículos de revistas sobre el tema "Hypergraph-structure"
Xu, Jinhuan, Liang Xiao y Jingxiang Yang. "Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image". Remote Sensing 13, n.º 7 (2 de abril de 2021): 1372. http://dx.doi.org/10.3390/rs13071372.
Texto completoFeng, Yifan, Haoxuan You, Zizhao Zhang, Rongrong Ji y Yue Gao. "Hypergraph Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 3558–65. http://dx.doi.org/10.1609/aaai.v33i01.33013558.
Texto completoLiu, Jian, Dong Chen, Jingyan Li y Jie Wu. "Neighborhood hypergraph model for topological data analysis". Computational and Mathematical Biophysics 10, n.º 1 (1 de enero de 2022): 262–80. http://dx.doi.org/10.1515/cmb-2022-0142.
Texto completoYang, Zhe, Liangkui Xu y Lei Zhao. "Multimodal Feature Fusion Based Hypergraph Learning Model". Computational Intelligence and Neuroscience 2022 (16 de mayo de 2022): 1–13. http://dx.doi.org/10.1155/2022/9073652.
Texto completoMahmood Shuker, Faiza. "Improved Blockchain Network Performance using Hypergraph Structure". Journal of Engineering and Applied Sciences 14, n.º 2 (20 de noviembre de 2019): 5579–84. http://dx.doi.org/10.36478/jeasci.2019.5579.5584.
Texto completoPeng, Hao, Cheng Qian, Dandan Zhao, Ming Zhong, Jianmin Han y Wei Wang. "Targeting attack hypergraph networks". Chaos: An Interdisciplinary Journal of Nonlinear Science 32, n.º 7 (julio de 2022): 073121. http://dx.doi.org/10.1063/5.0090626.
Texto completoXu, Xixia, Qi Zou y Xue Lin. "Adaptive Hypergraph Neural Network for Multi-Person Pose Estimation". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 3 (28 de junio de 2022): 2955–63. http://dx.doi.org/10.1609/aaai.v36i3.20201.
Texto completoHuang, Yuan, Liping Wang, Xueying Wang y Wei An. "Joint Probabilistic Hypergraph Matching Labeled Multi-Bernoulli Filter for Rigid Target Tracking". Applied Sciences 10, n.º 1 (20 de diciembre de 2019): 99. http://dx.doi.org/10.3390/app10010099.
Texto completoKosian, David A. y Leon A. Petrosyan. "Two-Level Cooperative Game on Hypergraph". Contributions to Game Theory and Management 14 (2021): 227–35. http://dx.doi.org/10.21638/11701/spbu31.2021.17.
Texto completoSiriwong, Pinkaew y Ratinan Boonklurb. "k-Zero-Divisor and Ideal-Based k-Zero-Divisor Hypergraphs of Some Commutative Rings". Symmetry 13, n.º 11 (20 de octubre de 2021): 1980. http://dx.doi.org/10.3390/sym13111980.
Texto completoTesis sobre el tema "Hypergraph-structure"
Datta, Sagnik. "Fully bayesian structure learning of bayesian networks and their hypergraph extensions". Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2283.
Texto completoIn this thesis, I address the important problem of the determination of the structure of complex networks, with the widely used class of Bayesian network models as a concrete vehicle of my ideas. The structure of a Bayesian network represents a set of conditional independence relations that hold in the domain. Learning the structure of the Bayesian network model that represents a domain can reveal insights into its underlying causal structure. Moreover, it can also be used for prediction of quantities that are difficult, expensive, or unethical to measure such as the probability of cancer based on other quantities that are easier to obtain. The contributions of this thesis include (A) a software developed in C language for structure learning of Bayesian networks; (B) introduction a new jumping kernel in the Metropolis-Hasting algorithm for faster sampling of networks (C) extending the notion of Bayesian networks to structures involving loops and (D) a software developed specifically to learn cyclic structures. Our primary objective is structure learning and thus the graph structure is our parameter of interest. We intend not to perform estimation of the parameters involved in the mathematical models
Sharma, Govind. "Hypergraph Network Models: Learning, Prediction, and Representation in the Presence of Higher-Order Relations". Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4781.
Texto completoCapítulos de libros sobre el tema "Hypergraph-structure"
Dai, Qionghai y Yue Gao. "Hypergraph Structure Evolution". En Artificial Intelligence: Foundations, Theory, and Algorithms, 101–20. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_6.
Texto completoDai, Qionghai y Yue Gao. "Hypergraph Computation Paradigms". En Artificial Intelligence: Foundations, Theory, and Algorithms, 41–47. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_3.
Texto completoDai, Qionghai y Yue Gao. "Hypergraph Modeling". En Artificial Intelligence: Foundations, Theory, and Algorithms, 49–71. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_4.
Texto completoDai, Qionghai y Yue Gao. "Neural Networks on Hypergraph". En Artificial Intelligence: Foundations, Theory, and Algorithms, 121–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_7.
Texto completoDai, Qionghai y Yue Gao. "Mathematical Foundations of Hypergraph". En Artificial Intelligence: Foundations, Theory, and Algorithms, 19–40. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_2.
Texto completoDai, Qionghai y Yue Gao. "The DeepHypergraph Library". En Artificial Intelligence: Foundations, Theory, and Algorithms, 237–40. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_12.
Texto completoDai, Qionghai y Yue Gao. "Typical Hypergraph Computation Tasks". En Artificial Intelligence: Foundations, Theory, and Algorithms, 73–99. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_5.
Texto completoDai, Qionghai y Yue Gao. "Hypergraph Computation for Medical and Biological Applications". En Artificial Intelligence: Foundations, Theory, and Algorithms, 191–221. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_10.
Texto completoKosian, David A. y Leon A. Petrosyan. "New Characteristic Function for Cooperative Games with Hypergraph Communication Structure". En Static & Dynamic Game Theory: Foundations & Applications, 87–98. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51941-4_7.
Texto completoMunshi, Shiladitya, Ayan Chakraborty y Debajyoti Mukhopadhyay. "Constraint Driven Stratification of RDF with Hypergraph Graph (HG(2)) Data Structure". En Communications in Computer and Information Science, 167–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36321-4_15.
Texto completoActas de conferencias sobre el tema "Hypergraph-structure"
Cai, Derun, Moxian Song, Chenxi Sun, Baofeng Zhang, Shenda Hong y Hongyan Li. "Hypergraph Structure Learning for Hypergraph Neural Networks". En Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/267.
Texto completoZhang, Zizhao, Haojie Lin y Yue Gao. "Dynamic Hypergraph Structure Learning". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/439.
Texto completoChang, Hyung Jin, Tobias Fischer, Maxime Petit, Martina Zambelli y Yiannis Demiris. "Kinematic Structure Correspondences via Hypergraph Matching". En 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.457.
Texto completoJiang, Jianwen, Yuxuan Wei, Yifan Feng, Jingxuan Cao y Yue Gao. "Dynamic Hypergraph Neural Networks". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/366.
Texto completoZhou, Peng, Zongqian Wu, Xiangxiang Zeng, Guoqiu Wen, Junbo Ma y Xiaofeng Zhu. "Totally Dynamic Hypergraph Neural Networks". En Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/275.
Texto completoKok, Stanley y Pedro Domingos. "Learning Markov logic network structure via hypergraph lifting". En the 26th Annual International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1553374.1553440.
Texto completoMunshi, Shiladitya, Ayan Chakraborty y Debajyoti Mukhopadhyay. "Theories of Hypergraph-Graph (HG(2)) Data Structure". En 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies (CUBE). IEEE, 2013. http://dx.doi.org/10.1109/cube.2013.45.
Texto completoLi, Shengkun, Dawei Du, Longyin Wen, Ming-Ching Chang y Siwei Lyu. "Hybrid structure hypergraph for online deformable object tracking". En 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296457.
Texto completoZhao, Yusheng, Xiao Luo, Wei Ju, Chong Chen, Xian-Sheng Hua y Ming Zhang. "Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting". En 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023. http://dx.doi.org/10.1109/icde55515.2023.00178.
Texto completoSu, Lifan, Yue Gao, Xibin Zhao, Hai Wan, Ming Gu y Jiaguang Sun. "Vertex-Weighted Hypergraph Learning for Multi-View Object Classification". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/387.
Texto completo