AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The proposed shared-encoder, distinct-decoder framework enables accurate and reliable joint estimation of multi-model dMRI microstructural parameters from accelerated acquisitions and provides a practical solution for fast microstructural imaging.
This work introduces CNsEMD, an expert-annotated multimodal dataset for CN parcellation, and proposes the projective hyperspherical manifold network (PHM-Net), which learns cross-modal representations by capturing angular relationships in a shared hyperspherical embedding space.
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