30 citations · 37 across the 6 of their papers we have counts for
6 papers
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…
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…
Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks
Xiangrui Li, Jasmine Hect, Moriah Thomason +1
Understanding human fetal neurodevelopment is of great clinical importance as abnormal development is linked to adverse neuropsychiatric outcomes after birth. Recent advances in fu…
CRCEN: A Generalized Cost-sensitive Neural Network Approach for Imbalanced Classification
Xiangrui Li, Dongxiao Zhu
Classification on imbalanced datasets is a challenging task in real-world applications. Training conventional classification algorithms directly by minimizing classification error…