Author

B. Goldsmith

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Jul 2026

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

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 parameter...

Cameron Gruich, Yao Weichi, Yixin Wang et al. · arXiv.org · 0 citations

#machine learning Preprint Sep 2026

Fixed-Dimensional Latent Flow for Generating Variable-Size 3D Molecules

In molecular discovery, molecule size is coupled to composition, structure, and other target properties. Yet most 3D generators require molecule size to be specified before generation. Here, we introduce Equivariant-Free Transformer-Autoencoded Latent Flow Matching, a two-stage generative framework that relies entirely...

Yao Weichi, Cameron J. Gruich, B. R. Goldsmith et al. · 0 citations