10 citations · 27 across the 6 of their papers we have counts for
6 papers
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare
Muchao Ye, Junyu Luo, Guanjie Zheng +3
Deep neural networks (DNNs) have been broadly adopted in health risk prediction to provide healthcare diagnoses and treatments. To evaluate their robustness, existing research cond…
FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update
Junyu Luo, Jianlei Yang, Xucheng Ye +2
Federated learning aims to protect users' privacy while performing data analysis from different participants. However, it is challenging to guarantee the training efficiency on het…
FedSiam: Towards Adaptive Federated Semi-Supervised Learning
Zewei Long, Liwei Che, Yaqing Wang +5
Federated learning (FL) has emerged as an effective technique to co-training machine learning models without actually sharing data and leaking privacy. However, most existing FL me…
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation
Junyu Luo, Zifei Zheng, Hanzhong Ye +5
Patients with low health literacy usually have difficulty understanding medical jargon and the complex structure of professional medical language. Although some studies are propose…
SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training
Pengcheng Dai, Jianlei Yang, Xucheng Ye +5
Training Convolutional Neural Networks (CNNs) usually requires a large number of computational resources. In this paper, \textit{SparseTrain} is proposed to accelerate CNN training…
Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets
Junyu Luo, Yong Xu, Chenwei Tang +1
The inverse mapping of GANs'(Generative Adversarial Nets) generator has a great potential value.Hence, some works have been developed to construct the inverse function of generator…