Chaofan Tao

944 total citations
12 papers, 169 citations indexed

About

Chaofan Tao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Chaofan Tao has authored 12 papers receiving a total of 169 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 1 paper in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Chaofan Tao's work include Domain Adaptation and Few-Shot Learning (5 papers), Topic Modeling (4 papers) and Advanced Neural Network Applications (3 papers). Chaofan Tao is often cited by papers focused on Domain Adaptation and Few-Shot Learning (5 papers), Topic Modeling (4 papers) and Advanced Neural Network Applications (3 papers). Chaofan Tao collaborates with scholars based in Hong Kong, China and Sweden. Chaofan Tao's co-authors include Joseph Y. Lo, Chaofan Chen, Cynthia Rudin, Fides R. Schwartz, Alina Jade Barnett, Ngai Wong, Lifeng Shang, Wei Zhang, Xin Jiang and Qun Liu and has published in prestigious journals such as Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

In The Last Decade

Chaofan Tao

10 papers receiving 161 citations

Peers

Chaofan Tao

AI

CVPR

RNMI

HI

CNC

Bojian Hou United States

T. Kar India

Meirui Jiang Hong Kong

Abhishek Das India

Jesús Alejandro Alzate-Grisales Colombia

Mahesh Pawar India

Nitesh Pradhan India

Mohammed Adnan Iraq

Chetana Srinivas India

Yuchen Zhuang United States

Bojian Hou United States

Chaofan Tao

136 ×1.1

AI

37 ×0.6

CVPR

39 ×1.3

RNMI

12 ×0.5

HI

8 ×1.1

CNC

Citations per year, relative to Chaofan Tao Chaofan Tao (= 1×) peers Bojian Hou

Countries citing papers authored by Chaofan Tao

Since Specialization

Citations

This map shows the geographic impact of Chaofan Tao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Chaofan Tao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chaofan Tao more than expected).

Fields of papers citing papers by Chaofan Tao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chaofan Tao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Chaofan Tao. The network helps show where Chaofan Tao may publish in the future.

Co-authorship network of co-authors of Chaofan Tao

This figure shows the co-authorship network connecting the top 25 collaborators of Chaofan Tao. A scholar is included among the top collaborators of Chaofan Tao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chaofan Tao. Chaofan Tao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Liu, Che, Xin Wang, Chaofan Tao, et al.. (2025). MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation. 14510–14527.

2.

Wu, Liang, et al.. (2024). Source-free domain adaptation with unrestricted source hypothesis. Pattern Recognition. 149. 110246–110246. 9 indexed citations

3.

Tao, Chaofan, Lu Hou, Haoli Bai, et al.. (2023). Structured Pruning for Efficient Generative Pre-trained Language Models. 10880–10895. 3 indexed citations

4.

Gao, Yizhao, Chaofan Tao, Fengbin Tu, et al.. (2023). DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 43(5). 1613–1617. 8 indexed citations

5.

Tao, Chaofan, Lu Hou, Wei Zhang, et al.. (2022). Compression of Generative Pre-trained Language Models via Quantization. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 4821–4836. 31 indexed citations

6.

Chen, Dongsheng, Chaofan Tao, Lu Hou, et al.. (2022). LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling. 7985–7997. 11 indexed citations

7.

Tao, Chaofan & Ngai Wong. (2022). ODG-Q: Robust Quantization via Online Domain Generalization. 2022 26th International Conference on Pattern Recognition (ICPR). 32. 1822–1828.

8.

Tao, Chaofan, et al.. (2022). FAT: Frequency-Aware Transformation for Bridging Full-Precision and Low-Precision Deep Representations. IEEE Transactions on Neural Networks and Learning Systems. 35(2). 2640–2654. 1 indexed citations

9.

Ren, Yuan, Rui Lin, Chang Liu, et al.. (2021). BATMANN: A Binarized-All-Through Memory-Augmented Neural Network for Efficient In-Memory Computing. 1–4. 2 indexed citations

10.

Barnett, Alina Jade, Fides R. Schwartz, Chaofan Tao, et al.. (2021). A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nature Machine Intelligence. 3(12). 1061–1070. 82 indexed citations

11.

Tao, Chaofan, et al.. (2021). LiteGT. 161–170. 5 indexed citations

12.

Bin, Yi, Yang Yang, Chaofan Tao, et al.. (2019). MR-NET: Exploiting Mutual Relation for Visual Relationship Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 8110–8117. 17 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact