193 papers with code • 4 benchmarks • 10 datasets

Substituting missing data with values according to some criteria.


Use these libraries to find Imputation models and implementations
4 papers
4 papers

Most implemented papers

Recurrent Neural Networks for Multivariate Time Series with Missing Values

PeterChe1990/GRU-D 6 Jun 2016

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

jsyoon0823/GAIN ICML 2018

Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN).

Unsupervised Data Imputation via Variational Inference of Deep Subspaces

adalca/neuron 8 Mar 2019

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

caow13/BRITS NeurIPS 2018

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 1 Jun 2015

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

locuslab/icnn ICML 2017

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

tigvarts/ucm ICLR 2019

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 6 May 2019

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

gzerveas/mvts_transformer 6 Oct 2020

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

SunChenxiSCX/ISMTS-Review 23 Oct 2020

Developing deep learning methods on EHRs data is critical for personalized treatment, precise diagnosis and medical management.