Survey of Hypergraph Neural Networks and Its Application to Action Recognition (CAAI International Conference on Artificial Intelligence, 2022) [paper] A Survey on Various Representation Learning of Hypergraph for Unsupervised Feature Selection (Data, Engineering and Applications, 2022) [paper] Hypergraph L...
However, existing graph neural networks frameworks are designed based on simple graphs, which limits their ability to handle data with complex correlations. Therefore, in some special cases, especially when the data have interdependence, the complexity of the data poses a significant challenge to ...
Hypergraph Survey Hypergraph Learning: Methods and Practices (TPAMI, 2022) [paper] More Recent Advances in (Hyper)Graph Partitioning (ACM Computing Surveys, 2022) [paper] Survey of Hypergraph Neural Networks and Its Application to Action Recognition (CAAI International Conference on Artificial Intelligenc...
2023, Electronics (Switzerland) A Survey of Recommender Systems Based on Hypergraph Neural Networks 2023, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) View all citing articles on ScopusView full text ...
A. Transformation and linearization techniques in optimization: a state-of-the-art survey. Mathematics 10, 283 (2022). Article Google Scholar Feng, S. et al. Hypergraph models of biological networks to identify genes critical to pathogenic viral response. BMC Bioinformatics 22, 1–21 (2021)....
On a technical level, machine learning (ML) based models create neural networks that relate the geometric graph structures from room walls to an adjacency graph (vector25,26 or pixel-based27) or use reinforcement learning to subdivide a space28. This results in a linear, one-sided generation ...
Based on these observations, a patent-driven method for product function deployment based on a hypergraph is proposed herein. In addition, this study is performed to combine data mining technology with networks to solve multifunctional configuration problems during product development. The innovations of...
It is also important to note that, in our experiments, NPs and HPs are used with classifiers (e.g., hypergraph neural networks) powerful enough to capture (dis)similarity even across differing scales. Each hyperedge is expected to be sampled [Math Processing Error]s|E| times, and each h...
Baltrušaitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 423–443 (2018) Article Google Scholar Makhzani, A., Shlens, J., Jaitly, N., et al.: Adversarial autoencoders. arXiv preprint ...
A survey on multi-modal summarization. ACM Comput. Surv. 2023, 55, 1–36. [Google Scholar] [CrossRef] Javed, H.; Sufyan Beg, M.; Akhtar, N. Multimodal summarization: A concise review. In Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies; ...