Performance Analysis
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss An Zhang
An Zhang and Leheng Sheng contribute equally to this work. Zhibo Cai is the corresponding author.
35adf1ae7eb5734122c84b7a9ea5cc13-AuthorFeedback.pdf
Moreover,10 we will add one more predictiveperformance measure: F1 score. If the attribution map accurately represents the importance of the pixels, the20 classifier must havelower predictiveperformance. In [4], the authors introduce attention map transfer and gradient transfer. The function of the loss functions34 are different. The intent behind our stochastic matching regularizer is to facilitate42 the transfer of relevant information and prevent overfitting.
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization
Our research expands on this area by examining non-smooth weakly-convex FCCO, where the outer function is weakly convex and non-decreasing, and the inner function is weakly-convex.
