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University of Chinese Academy of Sciences
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Dynamic adaptation in vision-language models can significantly boost performance while cutting down computational costs.
AutoPDE reveals that explicitly representing solver strategies can boost PDE solving reliability by over 14% compared to existing methods.
By fusing pixel-level pathology images with spatially-resolved gene expression data, STAMP unlocks a new level of molecular specificity in multimodal pathology representation learning.
