Deep Imputation

Deep imputation leverages deep learning to fill in missing values in datasets, particularly focusing on time series and multivariate data prevalent in fields like healthcare and remote sensing. Current research emphasizes the development and benchmarking of various deep neural network architectures, including recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs), often incorporating techniques like attention mechanisms and contrastive learning to improve imputation accuracy. These advancements are significant because accurate imputation enhances the reliability of downstream analyses and predictive models, improving the quality of research and practical applications across numerous scientific domains.