92 citations · 112 across the 12 of their papers we have counts for
13 papers
Rare Event Early Detection: A Dataset of Sepsis Onset for Critically Ill Trauma Patients
Yin Jin, Tucker R. Stewart, Deyi Zhou +5
Sepsis is a major public health concern due to its high morbidity, mortality, and cost. Its clinical outcome can be substantially improved through early detection and timely interv…
Intra-Modal Proxy Learning for Zero-Shot Visual Categorization with CLIP
Qi Qian, Yuanhong Xu, Juhua Hu
Vision-language pre-training methods, e.g., CLIP, demonstrate an impressive zero-shot performance on visual categorizations with the class proxy from the text embedding of the clas…
AugDMC: Data Augmentation Guided Deep Multiple Clustering
Jiawei Yao, Enbei Liu, Maham Rashid +1
Clustering aims to group similar objects together while separating dissimilar ones apart. Thereafter, structures hidden in data can be identified to help understand data in an unsu…
Multi-Subset Approach to Early Sepsis Prediction
Kevin Ewig, Xiangwen Lin, Tucker Stewart +4
Sepsis is a life-threatening organ malfunction caused by the host's inability to fight infection, which can lead to death without proper and immediate treatment. Therefore, early d…
NPRL: Nightly Profile Representation Learning for Early Sepsis Onset Prediction in ICU Trauma Patients
Tucker Stewart, Katherine Stern, Grant O'Keefe +2
Sepsis is a syndrome that develops in the body in response to the presence of an infection. Characterized by severe organ dysfunction, sepsis is one of the leading causes of mortal…
Improved Visual Fine-tuning with Natural Language Supervision
Junyang Wang, Yuanhong Xu, Juhua Hu +3
Fine-tuning a visual pre-trained model can leverage the semantic information from large-scale pre-training data and mitigate the over-fitting problem on downstream vision tasks wit…
Enhancing Peak Network Traffic Prediction via Time-Series Decomposition
Tucker Stewart, Bin Yu, Anderson Nascimento +1
For network administration and maintenance, it is critical to anticipate when networks will receive peak volumes of traffic so that adequate resources can be allocated to service r…
Unsupervised Visual Representation Learning by Online Constrained K-Means
Qi Qian, Yuanhong Xu, Juhua Hu +2
Cluster discrimination is an effective pretext task for unsupervised representation learning, which often consists of two phases: clustering and discrimination. Clustering is to as…
Improved Knowledge Distillation via Full Kernel Matrix Transfer
Qi Qian, Hao Li, Juhua Hu
Knowledge distillation is an effective way for model compression in deep learning. Given a large model (i.e., teacher model), it aims to improve the performance of a compact model…
Weakly Supervised Representation Learning with Coarse Labels
Yuanhong Xu, Qi Qian, Hao Li +2
With the development of computational power and techniques for data collection, deep learning demonstrates a superior performance over most existing algorithms on visual benchmark…
Hierarchically Robust Representation Learning
Qi Qian, Juhua Hu, Hao Li
With the tremendous success of deep learning in visual tasks, the representations extracted from intermediate layers of learned models, that is, deep features, attract much attenti…
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
Qi Qian, Lei Shang, Baigui Sun +3
Distance metric learning (DML) is to learn the embeddings where examples from the same class are closer than examples from different classes. It can be cast as an optimization prob…
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution
Lingyang Chu, Xia Hu, Juhua Hu +2
Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical. To re…