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We propose (layer-wise) feature imputation of the missing input values to a convolution.
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.
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.
Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems.
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.
In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework to model multivariate time series data.
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.
Multiple imputation by chained equations (MICE) is a flexible and practical approach to handling missing data.