2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2026
Adapting Evidential Neural Networks to Test-Time Neighbor Fusion Improves Molecular Property Prediction
Cameron Gruich, Weichi Yao, Yixin Wang +1
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 proc…
cs.LG2023★ 2 cited
Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantification
Cameron Gruich, Varun Madhavan, Yixin Wang +1
It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimati…