9 citations · 12 across the 6 of their papers we have counts for
6 papers
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation
Heath Arthur-Loui, Amina Mollaysa, Michael Krauthammer
De novo molecule design has become a highly active research area, advanced significantly through the use of state-of-the-art generative models. Despite these advances, several fund…
Attention-based Multi-task Learning for Base Editor Outcome Prediction
Amina Mollaysa, Ahmed Allam, Michael Krauthammer
Human genetic diseases often arise from point mutations, emphasizing the critical need for precise genome editing techniques. Among these, base editing stands out as it allows targ…
Attention-based Multi-task Learning for Base Editor Outcome Prediction
Amina Mollaysa, Ahmed Allam, Michael Krauthammer
Human genetic diseases often arise from point mutations, emphasizing the critical need for precise genome editing techniques. Among these, base editing stands out as it allows targ…
Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Series
Xingyu Chen, Xiaochen Zheng, Amina Mollaysa +3
Irregular multivariate time series data is characterized by varying time intervals between consecutive observations of measured variables/signals (i.e., features) and varying sampl…
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models
Manuel Schürch, Xiang Li, Ahmed Allam +4
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient…
Simple Contrastive Representation Learning for Time Series Forecasting
Xiaochen Zheng, Xingyu Chen, Manuel Schürch +3
Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification. However, these methods are less effective…