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CALM is a framework for learning interpretable associations between brain regions and genetic pathways from completely unpaired datasets, enabling biomarker discovery for neuropsychiatric disorders like autism without requiring paired multimodal data.
This paper proposes a cross-modal knowledge distillation framework that works without paired data by aligning feature and label distributions, offering theoretical guarantees and outperforming prior methods on multimodal benchmarks.