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Found 8 papers, 1 papers with code
Dynamic Local Attention with Hierarchical Patching for Irregular Clinical Time Series
Irregular multivariate time series data is prevalent in the clinical and healthcare domains.
Attention-based Multi-task Learning for Base Editor Outcome Prediction
To speed up this process, we present an attention-based two-stage machine learning model that learns to predict the likelihood of all possible editing outcomes for a given genomic target sequence.
Attention-based Multi-task Learning for Base Editor Outcome Prediction
To speed up this process, we present an attention-based two-stage machine learning model that learns to predict the likelihood of all possible editing outcomes for a given genomic target sequence.
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies.
SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting
Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification.
Goal-directed Generation of Discrete Structures with Conditional Generative Models
Unfortunately, maximum likelihood training of such models often fails with the samples from the generative model inadequately respecting the input properties.
Conditional generation of molecules from disentangled representations
Though machine learning approaches have shown great success in estimating properties of small molecules, the inverse problem of generating molecules with desired properties remains challenging.
Regularising Non-linear Models Using Feature Side-information
In this paper, we propose a framework that allows for the incorporation of the feature side-information during the learning of very general model families to improve the prediction performance.
