72 citations · 145 across the 8 of their papers we have counts for
8 papers
CARTL: Cooperative Adversarially-Robust Transfer Learning
Dian Chen, Hongxin Hu, Qian Wang +4
Transfer learning eases the burden of training a well-performed model from scratch, especially when training data is scarce and computation power is limited. In deep learning, a ty…
Recent Advances in Adversarial Training for Adversarial Robustness
Tao Bai, Jinqi Luo, Jun Zhao +2
Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models. Unlike other defense strategies, adversarial training…
Towards Privacy-assured and Lightweight On-chain Auditing of Decentralized Storage
Yuefeng Du, Huayi Duan, Anxin Zhou +3
How to audit outsourced data in centralized storage like cloud is well-studied, but it is largely under-explored for the rising decentralized storage network (DSN) that bodes well…
Augmenting Encrypted Search: A Decentralized Service Realization with Enforced Execution
Shengshan Hu, Chengjun Cai, Qian Wang +4
Searchable symmetric encryption (SSE) allows the data owner to outsource an encrypted database to a remote server in a private manner while maintaining the ability for selectively…
VeriML: Enabling Integrity Assurances and Fair Payments for Machine Learning as a Service
Lingchen Zhao, Qian Wang, Cong Wang +5
Machine Learning as a Service (MLaaS) allows clients with limited resources to outsource their expensive ML tasks to powerful servers. Despite the huge benefits, current MLaaS solu…
A Context-Aware User-Item Representation Learning for Item Recommendation
Libing Wu, Cong Quan, Chenliang Li +2
Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent repr…