Yuzhe Ma

83 papers receiving 1.6k citations

Hit Papers

Recent advances in convolutional neural network acceleration 2018 · 302 citations
3022018202620202023100200300

Peers

Yuzhe Ma
Comparison fields: 5 of 102
  • Hardware and Architecture 419
  • Industrial and Manufacturing Engineering 278
  • Electrical and Electronic Engineering 1.0k
  • Computer Vision and Pattern Recognition 317
  • Media Technology 120
Replace Hongbin Sun with:
Hongbin Sun China
Li Jiang China
Yangdong Deng China
Dimitrios Soudris Greece
Bin Ren United States
Jean-Didier Legat Belgium
Chong‐Min Kyung South Korea
Shawki Areibi Canada
Bhargab B. Bhattacharya India
Yuzhe Ma relative to Hongbin Sun China Hongbin Sun's profile →
Citations per field
00.5×9.0×
Hongbin Sun · 1×
Citations per year

Countries citing papers authored by Yuzhe Ma

Since Specialization
Citations

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

Fields of papers citing papers by Yuzhe Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Yuzhe Ma, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yuzhe Ma Line = papers co-authored together Yuzhe Ma links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20250
4 20250
5 20242
6 20247
7 20236
8 20231
9 20224
10 202118
11
Task-agnostic Exploration in Reinforcement Learning
20201
12
The Teaching Dimension of Q-learning
20203
13 20206
14 20198
15 201921
16 201832
17
A Unified Approximation Framework for Deep Neural Networks.
20183
18 201714
19 201721
20 20174

About Yuzhe Ma

Yuzhe Ma is a scholar working on Hardware and Architecture, Industrial and Manufacturing Engineering, Media Technology, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 98 papers that have together received 1.7k indexed citations. Recurring topics across this work include Advancements in Photolithography Techniques (36 papers), VLSI and FPGA Design Techniques (25 papers), Industrial Vision Systems and Defect Detection (17 papers), VLSI and Analog Circuit Testing (15 papers), Low-power high-performance VLSI design (11 papers), Image Processing Techniques and Applications (11 papers), Adversarial Robustness in Machine Learning (9 papers) and Integrated Circuits and Semiconductor Failure Analysis (9 papers). The work is most often cited by research in Hardware and Architecture (419 citations), Industrial and Manufacturing Engineering (278 citations), Electrical and Electronic Engineering (1.0k citations), Computer Vision and Pattern Recognition (317 citations) and Media Technology (120 citations). Yuzhe Ma has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Bei Yu, Haoyu Yang, Evangeline F. Y. Young, Tinghuan Chen, Meng Zhang, Zhifei Sun, Qianru Zhang, Shuhe Li, Xiaojin Zhu and Martin D. F. Wong. Their work appears in journals such as IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, ACM Transactions on Design Automation of Electronic Systems, Neurocomputing, Theoretical Computer Science and IEEE photonics journal.

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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