Neural Network Classifier

Neural network classifiers are computational models designed to categorize data into predefined classes, aiming for high accuracy and efficiency. Current research emphasizes improving interpretability, understanding generalization behavior (including phenomena like benign overfitting and neural collapse), and developing methods for uncertainty quantification and efficient computation of explainability metrics. These advancements are significant for various applications, ranging from medical diagnosis and text analysis to image recognition and more, driving progress in both theoretical understanding and practical deployment of these powerful tools.