3 citations · 3 across the 2 of their papers we have counts for
3 papers · 1 filter
MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models
Michael Maser, Natasa Tagasovska, Jae Hyeon Lee +1
Structure-based molecular ML (SBML) models can be highly sensitive to input geometries and give predictions with large variance. We present an approach to mitigate the challenge of…
SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers
Michael Maser, Ji Won Park, Joshua Yao-Yu Lin +3
We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers. We find that a non-contrastive (positive-pair only) auxiliary task ai…
Multi-segment preserving sampling for deep manifold sampler
Daniel Berenberg, Jae Hyeon Lee, Simon Kelow +6
Deep generative modeling for biological sequences presents a unique challenge in reconciling the bias-variance trade-off between explicit biological insight and model flexibility.…