Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems.

However, Does achieving a low ASR through current safety purification methods truly eliminate learned backdoor features from the pretraining phase? In this paper, we provide an affirmative answer to this question by thoroughly investigating the Post-Purification Robustness of current backdoor purification methods.

Dosing models often use differential equations to model biological dynamics.