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MTL-CMO is introduced, a multi-task framework that jointly learns shared function spaces and task-specific operators across multiple datasets and T-CMO, a transfer learning method that reuses the shared spaces to estimate, in closed form, the operator of a new conditional distribution.
This work addresses the Molecule Retrieval task, which consists in recovering the chemical structure of a metabolite from its MS/MS spectrum given a set of candidate molecules, and proposes a unified framework encompassing recent approaches based on representation alignment and contrastive learning.
This work proposes to amortize the graph matching (node alignment) problem and showcases the efficiency of this approach on toy and real world SGP problems of increasing complexity including a novel Mass-spectra to Scaffold task that is introduced.
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