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Found 4 papers, 0 papers with code
Blind Biological Sequence Denoising with Self-Supervised Set Learning
This set embedding represents the "average" of the subreads and can be decoded into a prediction of the clean sequence.
MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models
As we train our predictive models jointly with a conformer decoder, the new latent embeddings can be mapped to their corresponding inputs, which we call \textit{MoleCLUEs}, or (molecular) counterfactual latent uncertainty explanations \citep{antoran2020getting}.
SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers
We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers.
Multi-segment preserving sampling for deep manifold sampler
We introduce an alternative approach to this guided sampling procedure, multi-segment preserving sampling, that enables the direct inclusion of domain-specific knowledge by designating preserved and non-preserved segments along the input sequence, thereby restricting variation to only select regions.
