Imputation
193 papers with code • 4 benchmarks • 10 datasets
Substituting missing data with values according to some criteria.
Benchmarks
These leaderboards are used to track progress in Imputation
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Libraries
Use these libraries to find Imputation models and implementationsDatasets
WebKB
Sprites
MotionSense
PhysioNet Challenge 2012
Adult Data Set
Most implemented papers
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
GAIN: Missing Data Imputation using Generative Adversarial Nets
Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN).
Unsupervised Data Imputation via Variational Inference of Deep Subspaces
adalca/neuron
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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.
BRITS: Bidirectional Recurrent Imputation for Time Series
It is ubiquitous that time series contains many missing values.
Imaging Time-Series to Improve Classification and Imputation
cauchyturing/UCR_Time_Series_Classification_Deep_Learning_Baseline
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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.
Input Convex Neural Networks
We show that many existing neural network architectures can be made input-convex with a minor modification, and develop specialized optimization algorithms tailored to this setting.
Variational Autoencoder with Arbitrary Conditioning
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
YosefLab/scVI
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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.
A Transformer-based Framework for Multivariate Time Series Representation Learning
In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series.
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data
Developing deep learning methods on EHRs data is critical for personalized treatment, precise diagnosis and medical management.
