13 citations · 22 across the 5 of their papers we have counts for
5 papers
FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases
Chong Fu, Xuhong Zhang, Shouling Ji +4
Trojan attack on deep neural networks, also known as backdoor attack, is a typical threat to artificial intelligence. A trojaned neural network behaves normally with clean inputs.…
TextDefense: Adversarial Text Detection based on Word Importance Entropy
Lujia Shen, Xuhong Zhang, Shouling Ji +4
Currently, natural language processing (NLP) models are wildly used in various scenarios. However, NLP models, like all deep models, are vulnerable to adversarially generated text.…
Boosting Binary Neural Networks via Dynamic Thresholds Learning
Jiehua Zhang, Xueyang Zhang, Zhuo Su +5
Developing lightweight Deep Convolutional Neural Networks (DCNNs) and Vision Transformers (ViTs) has become one of the focuses in vision research since the low computational cost i…
Federated Submodel Optimization for Hot and Cold Data Features
Yucheng Ding, Chaoyue Niu, Fan Wu +4
We study practical data characteristics underlying federated learning, where non-i.i.d. data from clients have sparse features, and a certain client's local data normally involves…
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
Yikai Yan, Chaoyue Niu, Yucheng Ding +5
Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous clients collaboratively train a model without sharing training data. In this work…