arXiv Machine Learning July 14, 2026 By Cameron Gruich, Weichi Yao, Yixin Wang, Bryan Goldsmith

Adapting Evidential Neural Networks to Test-Time Neighbor Fusion Improves Molecular Property Prediction

Read the original on arXiv Machine Learning →

arXiv:2607. 11091v1 Announce Type: new Abstract: A trained molecular property model can be refined at test time by correcting each prediction with the measured labels of the most similar training molecules, a retraining-free procedure we call neighbor fusion; evidential neural networks make it principled by using their aleatoric and epistemic uncertainty to parameterize a Bayesian update.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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