18 citations · 21 across the 9 of their papers we have counts for
5 papers · 1 filter
DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing
Jiawei Zhang, Zhongzhu Chen, Huan Zhang +2
Diffusion models have been leveraged to perform adversarial purification and thus provide both empirical and certified robustness for a standard model. On the other hand, different…
DensePure: Understanding Diffusion Models towards Adversarial Robustness
Chaowei Xiao, Zhongzhu Chen, Kun Jin +6
Diffusion models have been recently employed to improve certified robustness through the process of denoising. However, the theoretical understanding of why diffusion models are ab…
How to Attain Communication-Efficient DNN Training? Convert, Compress, Correct
Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang +1
This paper introduces CO3 -- an algorithm for communication-efficient federated Deep Neural Network (DNN) training. CO3 takes its name from three processing applied which reduce th…
Convert, compress, correct: Three steps toward communication-efficient DNN training
Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang +1
In this paper, we introduce a novel algorithm, , for communication-efficiency distributed Deep Neural Network (DNN) training. is a joint training/com…
DNN gradient lossless compression: Can GenNorm be the answer?
Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang +1
In this paper, the problem of optimal gradient lossless compression in Deep Neural Network (DNN) training is considered. Gradient compression is relevant in many distributed DNN tr…