Public articles linked to the same research event.
arXiv The work proposes a Multimodal Hypergraph Flow Matching (MHG-FM) framework that builds modality-specific hypergraphs, learns higher-order representations with Hypergraph Neural Network (HGNN) encoders, performs bidirectional cross-modal fusion via Dual Cross-Attention (DCA), and maps fused representations into a compact latent space with a variational autoencoder, where conditional flow matching enables joint structural connectivity (SC) and functional connectivity (FC) generation and cross-modal translation; on the Human Connectome Project Young Adult (HCP-YA) dataset it outperforms several state-of-the-art baselines in reconstruction quality, topology preservation, distributional similarity, and SC-FC coupling, while sampling about 8x faster than a matched diffusion backbone.
The work proposes a Multimodal Hypergraph Flow Matching (MHG-FM) framework that builds modality-specific hypergraphs, learns higher-order representations with Hypergraph Neural Network (HGNN) encoders, performs bidirectional cross-modal fusion via Dual Cross-Attention (DCA), and maps fused representations into a compact latent space with a variational autoencoder, where conditional flow matching enables joint structural connectivity (SC) and functional connectivity (FC) generation and cross-modal translation; on the Human Connectome Project Young Adult (HCP-YA) dataset it outperforms several state-of-the-art baselines in reconstruction quality, topology preservation, distributional similarity, and SC-FC coupling, while sampling about 8x faster than a matched diffusion backbone.
The work proposes a Multimodal Hypergraph Flow Matching (MHG-FM) framework that builds modality-specific hypergraphs, learns higher-order representations with Hypergraph Neural Network (HGNN) encoders, performs bidirectional cross-modal fusion via Dual Cross-Attention (DCA), and maps fused representations into a compact latent space with a variational autoencoder, where conditional flow matching enables joint structural connectivity (SC) and functional connectivity (FC) generation and cross-modal translation; on the Human Connectome Project Young Adult (HCP-YA) dataset it outperforms several state-of-the-art baselines in reconstruction quality, topology preservation, distributional similarity, and SC-FC coupling, while sampling about 8x faster than a matched diffusion backbone.
The work proposes a Multimodal Hypergraph Flow Matching (MHG-FM) framework that builds modality-specific hypergraphs, learns higher-order representations with Hypergraph Neural Network (HGNN) encoders, performs bidirectional cross-modal fusion via Dual Cross-Attention (DCA), and maps fused representations into a compact latent space with a variational autoencoder, where conditional flow matching enables joint structural connectivity (SC) and functional connectivity (FC) generation and cross-modal translation; on the Human Connectome Project Young Adult (HCP-YA) dataset it outperforms several state-of-the-art baselines in reconstruction quality, topology preservation, distributional similarity, and SC-FC coupling, while sampling about 8x faster than a matched diffusion backbone.