Методи навчання нейроної мережі на неповних даних

Abstract

Attention-based deep learning networks enhance handling incomplete and low-quality data, advancing machine learning. The paper explores challenges and methodologies, emphasizing attention mechanisms for improved model accuracy. Incomplete and low-quality datasets in healthcare, finance, and environmental studies pose challenges to traditional models. Attention-based models address this by focusing on relevant data features, minimizing imperfections. Specialized neural network architectures are discussed for enhanced data processing efficiency. Continued research is crucial for optimizing these models, including advanced regularization and data augmentation.