82 citations · 234 across the 22 of their papers we have counts for
5 papers · 1 filter
Gradient-Congruity Guided Federated Sparse Training
Chris Xing Tian, Yibing Liu, Haoliang Li +2
Edge computing allows artificial intelligence and machine learning models to be deployed on edge devices, where they can learn from local data and collaborate to form a global mode…
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
Yibing Liu, Chris Xing Tian, Haoliang Li +2
The out-of-distribution (OOD) problem generally arises when neural networks encounter data that significantly deviates from the training data distribution, i.e., in-distribution (I…
Rethinking Attention-Model Explainability through Faithfulness Violation Test
Yibing Liu, Haoliang Li, Yangyang Guo +3
Attention mechanisms are dominating the explainability of deep models. They produce probability distributions over the input, which are widely deemed as feature-importance indicato…
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment
Chris Xing Tian, Haoliang Li, Yufei Wang +1
Deep neural networks (DNN) have demonstrated unprecedented success for medical imaging applications. However, due to the issue of limited dataset availability and the strict legal…
Neuron Coverage-Guided Domain Generalization
Chris Xing Tian, Haoliang Li, Xiaofei Xie +2
This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our m…