30 citations · 41 across the 7 of their papers we have counts for
7 papers
Negative Flux Aggregation to Estimate Feature Attributions
Xin Li, Deng Pan, Chengyin Li +2
There are increasing demands for understanding deep neural networks' (DNNs) behavior spurred by growing security and/or transparency concerns. Due to multi-layer nonlinearity of th…
Learning Compact Features via In-Training Representation Alignment
Xin Li, Xiangrui Li, Deng Pan +2
Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of the feature extractor (i.e., last hidden layer) and a linear classifier (i.e., output layer) that…
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints
Xin Li, Xiangrui Li, Deng Pan +1
Convolutional neural networks (CNNs) have achieved state-of-the-art performance on various tasks in computer vision. However, recent studies demonstrate that these models are vulne…
Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability
Deng Pan, Xiangrui Li, Xin Li +1
Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items. Recently, explainabl…
Defending against adversarial attacks on medical imaging AI system, classification or detection?
Xin Li, Deng Pan, Dongxiao Zhu
Medical imaging AI systems such as disease classification and segmentation are increasingly inspired and transformed from computer vision based AI systems. Although an array of adv…
On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks
Xiangrui Li, Xin Li, Deng Pan +1
Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision. When training d…