Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

Deep neural networks are susceptible to overfitting with noisy labels. Existing meta-learning-based methods for learning with noisy labels require additional clean validation data, resulting in high ...

Node classification is a crucial area in graph representation learning, with significant applications in real-world network scenarios. However, due to the complexity of the relationships among nodes ...