# Imputation   Edit

137 papers with code • 3 benchmarks • 7 datasets

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# Image Inpainting with Learnable Feature Imputation

2 Nov 2020

We propose (layer-wise) feature imputation of the missing input values to a convolution.

1,018

# Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction

2 Mar 2020

However, once the missing values are imputed, most existing methods do not consider the fidelity or confidence of the imputed values in the modeling of downstream tasks.

977

# A Transformer-based Framework for Multivariate Time Series Representation Learning

6 Oct 2020

In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series.

675

# Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation

7 Aug 2020

Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data.

675

# A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation

23 Mar 2020

Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems.

675

# Bayesian Temporal Factorization for Multidimensional Time Series Prediction

14 Oct 2019

In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series -- in particular spatiotemporal data -- in the presence of missing values.

675

# Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation

30 Apr 2021

In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework by introducing \textit{temporal variation} as a new regularization term into the completion of a third-order (sensor $\times$ time of day $\times$ day) tensor.

622

# A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

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.

520

# Imaging Time-Series to Improve Classification and Imputation

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.

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# Multiple imputation using chained equations: issues and guidance for practice

Multiple imputation by chained equations (MICE) is a flexible and practical approach to handling missing data.

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