3 citations · 4 across the 4 of their papers we have counts for
4 papers
Blind Biological Sequence Denoising with Self-Supervised Set Learning
Nathan Ng, Ji Won Park, Jae Hyeon Lee +3
Biological sequence analysis relies on the ability to denoise the imprecise output of sequencing platforms. We consider a common setting where a short sequence is read out repeated…
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.…