Ruizhou Ding

533 total citations
12 papers, 231 citations indexed

About

Ruizhou Ding is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Ruizhou Ding has authored 12 papers receiving a total of 231 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Ruizhou Ding's work include Advanced Neural Network Applications (11 papers), Machine Learning and Data Classification (5 papers) and Adversarial Robustness in Machine Learning (4 papers). Ruizhou Ding is often cited by papers focused on Advanced Neural Network Applications (11 papers), Machine Learning and Data Classification (5 papers) and Adversarial Robustness in Machine Learning (4 papers). Ruizhou Ding collaborates with scholars based in United States and China. Ruizhou Ding's co-authors include Diana Marculescu, Ting-Wu Chin, R.D. Blanton, Zhuo Chen, Bodhi Priyantha, Di Wang, Dimitrios Lymberopoulos, Jie Liu, Dimitrios Stamoulis and Rongye Shi and has published in prestigious journals such as ACM Transactions on Embedded Computing Systems, ACM Transactions on Reconfigurable Technology and Systems and arXiv (Cornell University).

In The Last Decade

Ruizhou Ding

12 papers receiving 227 citations

Peers

Ruizhou Ding

CVPR

AI

EEE

CNC

SP

Michael Figurnov United States

Peter Jin United States

Elad Hoffer Israel

Ting-Wu Chin United States

Yury Nahshan Israel

Zizheng Pan Australia

Yuhang Li China

Danlu Chen United States

Ziran Wei China

Jingcai Guo Hong Kong

Michael Figurnov United States

Ruizhou Ding

220 ×1.4

CVPR

153 ×1.1

AI

36 ×0.7

EEE

18 ×0.9

CNC

10 ×0.9

SP

Citations per year, relative to Ruizhou Ding Ruizhou Ding (= 1×) peers Michael Figurnov

Countries citing papers authored by Ruizhou Ding

Since Specialization

Citations

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

Fields of papers citing papers by Ruizhou Ding

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ruizhou Ding. 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 Ruizhou Ding. The network helps show where Ruizhou Ding may publish in the future.

Co-authorship network of co-authors of Ruizhou Ding

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

All Works

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12 of 12 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.

Chen, Zhuo, Jiyuan Zhang, Ruizhou Ding, & Diana Marculescu. (2020). ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection. 1169–1178. 7 indexed citations

3.

Chin, Ting-Wu, Ruizhou Ding, Cha Zhang, & Diana Marculescu. (2019). LeGR: Filter Pruning via Learned Global Ranking.. arXiv (Cornell University). 10 indexed citations

4.

Chin, Ting-Wu, Ruizhou Ding, & Diana Marculescu. (2019). AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling. 1. 431–441. 7 indexed citations

5.

Stamoulis, Dimitrios, Ruizhou Ding, Di Wang, et al.. (2019). Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization. arXiv (Cornell University). 27 indexed citations

6.

Ding, Ruizhou, et al.. (2019). FLightNNs. 1–6. 12 indexed citations

7.

Ding, Ruizhou, et al.. (2019). Regularizing Activation Distribution for Training Binarized Deep Networks. 11400–11409. 86 indexed citations

8.

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

9.

Ding, Ruizhou, et al.. (2018). Quantized deep neural networks for energy efficient hardware-based inference. 1–8. 33 indexed citations

10.

Ding, Ruizhou, et al.. (2018). Lightening the Load with Highly Accurate Storage- and Energy-Efficient LightNNs. ACM Transactions on Reconfigurable Technology and Systems. 11(3). 1–24. 9 indexed citations

11.

Ding, Ruizhou, et al.. (2017). Enhancing precipitation models by capturing multivariate and multiscale climate dynamics. 39–42. 1 indexed citations

12.

Ding, Ruizhou, et al.. (2017). LightNN. 35–40. 20 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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