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MABLE combines masked reconstruction with cosine-similarity losses to learn node and graph embeddings from large heterogeneous graphs, demonstrated on geospatial mineral-exploration data. It unifies masked autoencoding and metric learning in a self-supervised framework without requiring labeled data.
MuSViT is the first foundation vision model for sheet music, pre-trained on millions of pages via Masked Autoencoders, achieving superior performance in score recognition and symbol detection tasks.
NERVE proposes a network-aware bilinear tokenization method for self-supervised learning on brain functional connectivity matrices using masked autoencoders, improving representation learning across developmental cohorts.