1 code implementation • COLING 2022 • YuCheng Huang, Kai He, Yige Wang, Xianli Zhang, Tieliang Gong, Rui Mao, Chen Li
Second, the referents may be far from representing corresponding entity classes due to the label scarcity in the few-shot setting.
1 code implementation • 2 May 2023 • Yuxin Dong, Tieliang Gong, Hong Chen, Chen Li
However, the current generalization error bounds within this framework are still far from optimal, while substantial improvements on these bounds are quite challenging due to the intractability of high-dimensional information quantities.
no code implementations • 20 Feb 2023 • Jun Chen, Hong Chen, Xue Jiang, Bin Gu, Weifu Li, Tieliang Gong, Feng Zheng
Triplet learning, i. e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e. g., face recognition and person re-identification.
1 code implementation • 30 Nov 2022 • Yuxin Dong, Tieliang Gong, Shujian Yu, Hong Chen, Chen Li
The matrix-based R\'enyi's entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution.
no code implementations • 16 May 2022 • Yuxin Dong, Tieliang Gong, Shujian Yu, Chen Li
The Matrix-based Renyi's entropy enables us to directly measure information quantities from given data without the costly probability density estimation of underlying distributions, thus has been widely adopted in numerous statistical learning and inference tasks.
no code implementations • 9 Mar 2022 • Xuebin Zhao, Hong Chen, Yingjie Wang, Weifu Li, Tieliang Gong, Yulong Wang, Feng Zheng
Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings.
no code implementations • 27 Dec 2021 • Tieliang Gong, Yuxin Dong, Shujian Yu, Bo Dong
The recently developed matrix based Renyi's entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi definite (PSD) matrices in reproducing kernel Hilbert space, without estimation of the underlying data distribution.
no code implementations • 12 Dec 2021 • Tieliang Gong, Yuxin Dong, Hong Chen, Bo Dong, Chen Li
Subsampling is an important technique to tackle the computational challenges brought by big data.
no code implementations • 24 Nov 2021 • Jiangbo Shi, Chang Jia, Zeyu Gao, Tieliang Gong, Chunbao Wang, Chen Li
However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain.
no code implementations • 9 Nov 2021 • Jialun Wu, Anyu Mao, Xinrui Bao, Haichuan Zhang, Zeyu Gao, Chunbao Wang, Tieliang Gong, Chen Li
However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured description of relevant patient information.
no code implementations • 26 Oct 2021 • Jialun Wu, Yang Liu, Zeyu Gao, Tieliang Gong, Chunbao Wang, Chen Li
To address this issue, we propose Biomedical Information Extraction, a hybrid neural network to extract relations from biomedical text and unstructured medical reports.
no code implementations • 26 Oct 2021 • Jialun Wu, Haichuan Zhang, Zeyu Gao, Xinrui Bao, Tieliang Gong, Chunbao Wang, Chen Li
Tumor region detection, subtype and grade classification are the fundamental diagnostic indicators for renal cell carcinoma (RCC) in whole-slide images (WSIs).
no code implementations • 26 Oct 2021 • Anyu Mao, Jialun Wu, Xinrui Bao, Zeyu Gao, Tieliang Gong, Chen Li
In order to take advantage of segmentation methods based on point annotation, further alleviate the manual workload, and make cancer diagnosis more efficient and accurate, it is necessary to develop an automatic nucleus detection algorithm, which can automatically and efficiently locate the position of the nucleus in the pathological image and extract valuable information for pathologists.
no code implementations • 26 Oct 2021 • Jialun Wu, Zeyu Gao, Haichuan Zhang, Ruonan Zhang, Tieliang Gong, Chunbao Wang, Chen Li
In this study, we propose a framework that combines pathological images and medical reports to generate a personalized diagnosis result for individual patient.
no code implementations • NeurIPS 2020 • Yingjie Wang, Hong Chen, Feng Zheng, Chen Xu, Tieliang Gong, Yanhong Chen
For high-dimensional observations in real environment, e. g., Coronal Mass Ejections (CMEs) data, the learning performance of previous methods may be degraded seriously due to the complex non-Gaussian noise and the insufficiency of prior knowledge on variable structure.