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Can deep learning beat numerical weather prediction?
The recent hype about artificial intelligence has sparked renewed interest in applying the
successful deep learning (DL) methods for image recognition, speech recognition, robotics …
successful deep learning (DL) methods for image recognition, speech recognition, robotics …
Handling missing data with graph representation learning
Abstract Machine learning with missing data has been approached in many different ways,
including feature imputation where missing feature values are estimated based on observed …
including feature imputation where missing feature values are estimated based on observed …
Deep learning for missing value imputation of continuous data and the effect of data discretization
WC Lin, CF Tsai, JR Zhong - Knowledge-Based Systems, 2022 - Elsevier
Often real-world datasets are incomplete and contain some missing attribute values.
Furthermore, many data mining and machine learning techniques cannot directly handle …
Furthermore, many data mining and machine learning techniques cannot directly handle …
Review for Handling Missing Data with special missing mechanism
Missing data poses a significant challenge in data science, affecting decision-making
processes and outcomes. Understanding what missing data is, how it occurs, and why it is …
processes and outcomes. Understanding what missing data is, how it occurs, and why it is …
Missing data imputation with adversarially-trained graph convolutional networks
Missing data imputation (MDI) is the task of replacing missing values in a dataset with
alternative, predicted ones. Because of the widespread presence of missing data, it is a …
alternative, predicted ones. Because of the widespread presence of missing data, it is a …
Missing value estimation using clustering and deep learning within multiple imputation framework
Missing values in tabular data restrict the use and performance of machine learning,
requiring the imputation of missing values. Arguably the most popular imputation algorithm …
requiring the imputation of missing values. Arguably the most popular imputation algorithm …
Graph convolutional networks for graphs containing missing features
Abstract Graph Convolutional Network (GCN) has experienced great success in graph
analysis tasks. It works by smoothing the node features across the graph. The current GCN …
analysis tasks. It works by smoothing the node features across the graph. The current GCN …
Imputation of missing data with class imbalance using conditional generative adversarial networks
Missing data is a common problem faced with real-world datasets. Imputation is a widely
used technique to estimate the missing data. State-of-the-art imputation approaches model …
used technique to estimate the missing data. State-of-the-art imputation approaches model …
RETRACTED ARTICLE: Prediction of gestational diabetes based on explainable deep learning and fog computing
Gestational diabetes mellitus (GDM) is one of the pregnancy complications that endangers
both mothers and babies. GDM is usually diagnosed at 22–26 weeks of gestation. However …
both mothers and babies. GDM is usually diagnosed at 22–26 weeks of gestation. However …
Practical applications of deep learning to impute heterogeneous drug discovery data
Contemporary deep learning approaches still struggle to bring a useful improvement in the
field of drug discovery because of the challenges of sparse, noisy, and heterogeneous data …
field of drug discovery because of the challenges of sparse, noisy, and heterogeneous data …