3 accepted papers
Deep neural networks are vulnerable to backdoor attacks, where malicious behaviors are implanted during training. While existing defenses can effectively purify compromised models, they typically require labeled data or specific training procedures, making them difficult to apply beyond supervised l…
Machine learning models are vulnerable to membership inference attacks (MIAs), where adversaries attempt to predict whether specific samples are part of the model’s training set. Previous studies have demonstrated a strong correlation between the distinguishability of training and testing loss distr…
Session-based Recommendation aims to reveal the item distribution patterns in anonymous session sequences. Most existing approaches model the distribution patterns by utilizing either sequential or structural information individually to absorb different pattern knowledge, which can only model the di…