11 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Robust and sparse support vector machine via hybrid truncated loss for supervised classification
Yuliang Yang, Chen Chen, Yuxiang Liu +1
The support vector machine (SVM) is a widely used classifier, but choosing an appropriate loss function remains difficult. Convex losses such as the hinge loss and least-squares lo…
cs.CV2023★ 11 cited
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning
Jidong Ge, Yuxiang Liu, Jie Gui +5
Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations…
cs.CV2021
Delving into Variance Transmission and Normalization: Shift of Average Gradient Makes the Network Collapse
Yuxiang Liu, Jidong Ge, Chuanyi Li +1
Normalization operations are essential for state-of-the-art neural networks and enable us to train a network from scratch with a large learning rate (LR). We attempt to explain the…