63 citations · 202 across the 28 of their papers we have counts for
6 papers · 1 filter
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…
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…
COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays
Xin Li, Chengyin Li, Dongxiao Zhu
During the COVID-19 pandemic, there has been an emerging need for rapid, dedicated, and point-of-care COVID-19 patient disposition techniques to optimize resource utilization and c…
Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach
Yao Qiang, Xin Li, Dongxiao Zhu
Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representati…
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…
Improve SGD Training via Aligning Mini-batches
Xiangrui Li, Deng Pan, Xin Li +1
Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of a feature extractor (i.e. last hidden layer) and a linear classifier (i.e. output layer) that is…