Imputation
335 papers with code • 4 benchmarks • 11 datasets
Substituting missing data with values according to some criteria.
Benchmarks
These leaderboards are used to track progress in Imputation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Imputation models and implementationsDatasets
WebKB
Sprites
Adult
MotionSense
PhysioNet Challenge 2012
PulseImpute
Most implemented papers
Medical Image Imputation from Image Collections
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing.
Embedding Multimodal Relational Data for Knowledge Base Completion
In this paper, we propose multimodal knowledge base embeddings (MKBE) that use different neural encoders for this variety of observed data, and combine them with existing relational models to learn embeddings of the entities and multimodal data.
A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements
khalilouardini/Cassiopeia_Transcriptome
•
•
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.
Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN
Thanks to the recent success of generative adversarial network (GAN) for image synthesis, there are many exciting GAN approaches that successfully synthesize MR image contrast from other images with different contrasts.
Generative Imputation and Stochastic Prediction
mkachuee/GenerativeImputationStochasticPrediction
•
•
In order to make imputations, we train a simple and effective generator network to generate imputations that a discriminator network is tasked to distinguish.
CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation
WenjieDu/PyPOTS
•
•
In order to jointly capture the self-attention across multiple dimensions, including time, location and the sensor measurements, while maintain low computational complexity, we propose a novel approach called Cross-Dimensional Self-Attention (CDSA) to process each dimension sequentially, yet in an order-independent manner.
GP-VAE: Deep Probabilistic Time Series Imputation
ratschlab/GP-VAE
•
•
Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years.
Bayesian Temporal Factorization for Multidimensional Time Series Prediction
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.
Graph Markov Network for Traffic Forecasting with Missing Data
zhiyongc/Graph-Markov-Network
•
•
Although missing values can be imputed, existing data imputation methods normally need long-term historical traffic state data.
Lipschitz standardization for multivariate learning
adrianjav/lipschitz-standardization
•
•
While MTL solutions do not directly apply in the probabilistic setting (as they cannot handle the likelihood constraints) we show that similar ideas may be leveraged during data preprocessing.
