63 citations · 202 across the 28 of their papers we have counts for
15 papers · 1 filter
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…
Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation
Chengyin Li, Zheng Dong, Nathan Fisher +1
Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to all…
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification
Yao Qiang, Supriya Tumkur Suresh Kumar, Marco Brocanelli +1
On-device Deep Neural Networks (DNNs) have recently gained more attention due to the increasing computing power of the mobile devices and the number of applications in Computer Vis…
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…
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…
Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study
Najibesadat Sadati, Milad Zafar Nezhad, Ratna Babu Chinnam +1
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by…
Tackling Ordinal Regression Problem for Heterogeneous Data: Sparse and Deep Multi-Task Learning Approaches
Lu Wang, Dongxiao Zhu
Many real-world datasets are labeled with natural orders, i.e., ordinal labels. Ordinal regression is a method to predict ordinal labels that finds a wide range of applications in…
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…
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer
Milad Zafar Nezhad, Najibesadat Sadati, Kai Yang +1
Survival analysis has been developed and applied in the number of areas including manufacturing, finance, economics and healthcare. In healthcare domain, usually clinical data are…
Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study
Najibesadat Sadati, Milad Zafar Nezhad, Ratna Babu Chinnam +1
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by…
SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine
Milad Zafar Nezhad, Dongxiao Zhu, Najibesadat Sadati +2
Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different pa…
SAFS: A Deep Feature Selection Approach for Precision Medicine
Milad Zafar Nezhad, Dongxiao Zhu, Xiangrui Li +2
In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstracti…