Sparse Data

Sparse data, characterized by limited or missing observations, presents a significant challenge across numerous scientific and engineering domains. Current research focuses on developing robust methods to handle this scarcity, employing techniques like generative adversarial networks (GANs), probabilistic frameworks, and novel neural network architectures (e.g., UNet modifications, Transformers) tailored for sparse data imputation, reconstruction, and prediction. These advancements are crucial for improving the accuracy and reliability of models in various applications, ranging from climate modeling and personalized medicine to traffic flow prediction and intelligent tutoring systems.