A contrastive self-supervised loss is shown to be equivalent to learning the evolution operator's spectral decomposition, recovering slow modes in proteins, ligand binding, and ENSO climate data.
Neural conditional probability for uncertainty quantification.Advances in Neural Information Processing Systems, 37:60999–61039, 2024
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Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems
A contrastive self-supervised loss is shown to be equivalent to learning the evolution operator's spectral decomposition, recovering slow modes in proteins, ligand binding, and ENSO climate data.