85 citations · 361 across the 21 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…