Cross-modal brain networks characterize the complex connections between different brain regions from both functional and structural perspectives, which is of significant importance for brain network analysis and the diagnosis of brain diseases. However, existing methods have failed to fully exploit the complementary in...
Jing-Xi Feng, He-Ming Xu, Rundong Xue et al.· Proceedings of the Thirty-Fi...· 0 citations
An information bottleneck-guided node-level adaptive fusion employs the IB principle to learn independent weights for each node, facilitating the fine-grained integration of high-order information and global information to obtain an efficient representation for downstream tasks.
Jing-Xi Feng, Xu-Dong Chen, Yi-Fan Zhang et al.· 0 citations
Hyper-Fold is introduced, a rank-K separable convolutional backbone approaching this ceiling at message-passing cost, suggesting that a sufficiently expressive 3D backbone recovers information that fusion architectures previously borrowed from evolution-scale pretraining.
Yifan Feng, Guang Cheng, Shihui Ying et al.· 0 citations
Protein structure modeling rests on a single computational primitive: the interaction between what a residue is (sequence content) and where it sits (three-dimensional geometry). What is the expressive limit of this layer class? We show that the complete bilinear operator over content-geometry outer products--the suffi...
Yifan Feng, Guang Cheng, Shihui Ying et al.· 0 citations
The Hypergraph Identity-Aware Subtree (IA Subtree) Kernel is introduced, which distinguishes uniform-regular hypergraphs by considering both neighborhood connectivity and connection density and develops two Hypergraph Neural Networks: Hypergraph Isomorphism Networks (HGIN) and Identity-Aware Hypergraph Isomorphism Netw...
Yifan Feng, Rizhuo Huang, Yifan Zhang et al.· IEEE Transactions on Pattern...· 1 citation
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