Ting-Wu Chin

567 total citations
13 papers, 226 citations indexed

About

Ting-Wu Chin is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Ting-Wu Chin has authored 13 papers receiving a total of 226 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 4 papers in Electrical and Electronic Engineering. Recurrent topics in Ting-Wu Chin's work include Advanced Neural Network Applications (10 papers), Domain Adaptation and Few-Shot Learning (6 papers) and CCD and CMOS Imaging Sensors (3 papers). Ting-Wu Chin is often cited by papers focused on Advanced Neural Network Applications (10 papers), Domain Adaptation and Few-Shot Learning (6 papers) and CCD and CMOS Imaging Sensors (3 papers). Ting-Wu Chin collaborates with scholars based in United States, Taiwan and Germany. Ting-Wu Chin's co-authors include Diana Marculescu, Ruizhou Ding, Matthew Halpern, Vijay Janapa Reddi, Zhuo Chen, Anand Krishnan Prakash, Dimitrios Stamoulis, Cha Zhang, R.D. Blanton and Shiao‐Li Tsao and has published in prestigious journals such as IEEE Micro, ACM Transactions on Embedded Computing Systems and arXiv (Cornell University).

In The Last Decade

Ting-Wu Chin

12 papers receiving 223 citations

Peers

Ting-Wu Chin
Michael Figurnov United States
Peter Jin United States
Qinghao Hu China
Ruizhou Ding United States
Jiazhen Lin China
Yury Nahshan Israel
Zhenglun Kong United States
Tianyun Zhang United States
Loc N. Huynh Singapore
Gwangtae Park South Korea
Michael Figurnov United States
Ting-Wu Chin
Citations per year, relative to Ting-Wu Chin Ting-Wu Chin (= 1×) peers Michael Figurnov

Countries citing papers authored by Ting-Wu Chin

Since Specialization
Citations

This map shows the geographic impact of Ting-Wu Chin'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 Ting-Wu Chin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ting-Wu Chin more than expected).

Fields of papers citing papers by Ting-Wu Chin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ting-Wu Chin. 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 Ting-Wu Chin. The network helps show where Ting-Wu Chin may publish in the future.

Co-authorship network of co-authors of Ting-Wu Chin

This figure shows the co-authorship network connecting the top 25 collaborators of Ting-Wu Chin. A scholar is included among the top collaborators of Ting-Wu Chin 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 Ting-Wu Chin. Ting-Wu Chin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Jain, Aman, et al.. (2022). QUIDAM: A Framework for Qu ant i zation-aware D NN A ccelerator and M odel Co-Exploration. ACM Transactions on Embedded Computing Systems. 22(2). 1–21. 1 indexed citations
2.
Liang, Feng, et al.. (2022). ANT: Adapt Network Across Time for Efficient Video Processing. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 193. 2602–2607. 2 indexed citations
3.
Chin, Ting-Wu, Cha Zhang, & Diana Marculescu. (2021). Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness. 3237–3246. 6 indexed citations
4.
Chin, Ting-Wu, Diana Marculescu, & Ari S. Morcos. (2021). Width transfer: on the (in)variance of width optimization. 2984–2993. 2 indexed citations
5.
Chin, Ting-Wu, Ruizhou Ding, Cha Zhang, & Diana Marculescu. (2019). LeGR: Filter Pruning via Learned Global Ranking.. arXiv (Cornell University). 10 indexed citations
6.
Chin, Ting-Wu, Ruizhou Ding, & Diana Marculescu. (2019). AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling. 1. 431–441. 7 indexed citations
7.
Ding, Ruizhou, et al.. (2019). FLightNNs. 1–6. 12 indexed citations
8.
Ding, Ruizhou, et al.. (2019). Regularizing Activation Distribution for Training Binarized Deep Networks. 11400–11409. 86 indexed citations
9.
Chin, Ting-Wu, et al.. (2018). Domain-Specific Approximation for Object Detection. IEEE Micro. 38(1). 31–40. 9 indexed citations
10.
Chen, Zhuo, Ruizhou Ding, Ting-Wu Chin, & Diana Marculescu. (2018). Understanding the Impact of Label Granularity on CNN-Based Image Classification. 895–904. 18 indexed citations
11.
Stamoulis, Dimitrios, et al.. (2018). Designing adaptive neural networks for energy-constrained image classification. 1–8. 37 indexed citations
12.
Chin, Ting-Wu, et al.. (2017). Improving the accuracy of the leakage power estimation of embedded CPUs. 34. 1233–1236.
13.
Chin, Ting-Wu, et al.. (2017). Flying IoT: Toward Low-Power Vision in the Sky. IEEE Micro. 37(6). 40–51. 36 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.

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2026