Missing Half

"Missing Half" research explores how to effectively utilize incomplete data in various machine learning tasks, aiming to improve model robustness and performance even when information is scarce. Current efforts focus on developing novel data augmentation techniques (like "You Only Need Half"), robust model architectures (including those based on graph neural networks and cascaded systems), and improved evaluation metrics that account for missing data. This research is significant because it addresses the limitations of traditional methods that assume complete data, leading to more reliable and efficient algorithms across diverse applications such as image inpainting, speech recognition, and energy consumption prediction.