158 citations · 716 across the 49 of their papers we have counts for
8 papers · 1 filter
Neural Collapse Inspired Attraction-Repulsion-Balanced Loss for Imbalanced Learning
Liang Xie, Yibo Yang, Deng Cai +1
Class imbalance distribution widely exists in real-world engineering. However, the mainstream optimization algorithms that seek to minimize error will trap the deep learning model…
Attacking Adversarial Attacks as A Defense
Boxi Wu, Heng Pan, Li Shen +6
It is well known that adversarial attacks can fool deep neural networks with imperceptible perturbations. Although adversarial training significantly improves model robustness, fai…
Reducing the Teacher-Student Gap via Spherical Knowledge Disitllation
Jia Guo, Minghao Chen, Yao Hu +3
Knowledge distillation aims at obtaining a compact and effective model by learning the mapping function from a much larger one. Due to the limited capacity of the student, the stud…
Do Wider Neural Networks Really Help Adversarial Robustness?
Boxi Wu, Jinghui Chen, Deng Cai +2
Adversarial training is a powerful type of defense against adversarial examples. Previous empirical results suggest that adversarial training requires wider networks for better per…
Out-of-distribution Generalization via Partial Feature Decorrelation
Xin Guo, Zhengxu Yu, Chao Xiang +5
Most deep-learning-based image classification methods assume that all samples are generated under an independent and identically distributed (IID) setting. However, out-of-distribu…
Learning Graph-Level Representation for Drug Discovery
Junying Li, Deng Cai, Xiaofei He
Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a central problem of small-molecule based drug discovery. Molecules can be represented as…
Geodesic Distance Function Learning via Heat Flow on Vector Fields
Binbin Lin, Ji Yang, Xiaofei He +1
Learning a distance function or metric on a given data manifold is of great importance in machine learning and pattern recognition. Many of the previous works first embed the manif…
O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions
Lijun Zhang, Tianbao Yang, Rong Jin +1
Traditional algorithms for stochastic optimization require projecting the solution at each iteration into a given domain to ensure its feasibility. When facing complex domains, suc…