26 citations · 69 across the 10 of their papers we have counts for
10 papers
Prototype-Anchored Learning for Learning with Imperfect Annotations
Xiong Zhou, Xianming Liu, Deming Zhai +3
The success of deep neural networks greatly relies on the availability of large amounts of high-quality annotated data, which however are difficult or expensive to obtain. The resu…
Learning Towards the Largest Margins
Xiong Zhou, Xianming Liu, Deming Zhai +3
One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power. Th…
Weakly Supervised High-Fidelity Clothing Model Generation
Ruili Feng, Cheng Ma, Chengji Shen +5
The development of online economics arouses the demand of generating images of models on product clothes, to display new clothes and promote sales. However, the expensive proprieta…
2nd Place Solution for Waymo Open Dataset Challenge -- Real-time 2D Object Detection
Yueming Zhang, Xiaolin Song, Bing Bai +8
In an autonomous driving system, it is essential to recognize vehicles, pedestrians and cyclists from images. Besides the high accuracy of the prediction, the requirement of real-t…
Asymmetric Loss Functions for Learning with Noisy Labels
Xiong Zhou, Xianming Liu, Junjun Jiang +2
Robust loss functions are essential for training deep neural networks with better generalization power in the presence of noisy labels. Symmetric loss functions are confirmed to be…
ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
Liantao Ma, Chaohe Zhang, Yasha Wang +6
Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based so…
AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration
Liantao Ma, Junyi Gao, Yasha Wang +6
Deep learning-based health status representation learning and clinical prediction have raised much research interest in recent years. Existing models have shown superior performanc…
Multimodal Machine Learning for Automated ICD Coding
Keyang Xu, Mike Lam, Jingzhi Pang +9
This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modali…
SupportNet: solving catastrophic forgetting in class incremental learning with support data
Yu Li, Zhongxiao Li, Lizhong Ding +5
A plain well-trained deep learning model often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as catastrophic…
Predicting Discharge Medications at Admission Time Based on Deep Learning
Yuan Yang, Pengtao Xie, Xin Gao +4
Predicting discharge medications right after a patient being admitted is an important clinical decision, which provides physicians with guidance on what type of medication regimen…