193 papers with code • 4 benchmarks • 10 datasets
Substituting missing data with values according to some criteria.
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
In this work, we introduce a general probabilistic model that describes sparse high dimensional imaging data as being generated by a deep non-linear embedding.
We used Tiled Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn high-level features from the individual and compound GASF-GADF-MTF images.
We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot".
A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements
Building upon domain adaptation work, we propose gimVI, a deep generative model for the integration of spatial transcriptomic data and scRNA-seq data that can be used to impute missing genes.
In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series.
Developing deep learning methods on EHRs data is critical for personalized treatment, precise diagnosis and medical management.