Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science

HIGHLIGHTS

• who: Charlotte Loh from the Massachusetts Institute have published the research work: Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science, in the Journal: NATURE COMMUNICATIONS NATURE COMMUNICATIONS
• what: The authors seek to train a neural_network to predict desired properties (or labels) y from input x using minimal training data. | https://doi.org/10.1038/s41467-022-31915-y t More precisely, for a target problem Dt=fxi; y i gi=1 consisting of Nt input-label pairs, the authors focus on problem spaces where Nt is too small to successfully train . . .
  

   

  

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