Cheng Fu

858 citations
21 papers · 487 indexed · h-index 9
Topics
Adversarial Robustness in Machine Learning (7 papers)Physical Unclonable Functions (PUFs) and Hardware Security (5 papers)Advanced Neural Network Applications (3 papers)
Partner nations
United StatesChinaIsrael

In The Last Decade

Cheng Fu

20 papers receiving 464 citations

Peers

Cheng Fu
Comparison fields: 5 of 60
  • Artificial Intelligence 339
  • Computer Vision and Pattern Recognition 122
  • Signal Processing 82
  • Management Science and Operations Research 74
  • Hardware and Architecture 65
Replace Tingsong Jiang with:
Tingsong Jiang China
Quan Gan China
Amit Sabne United States
E. Wes Bethel United States
Andrew Ilyas United States
Mohamed Wahib Japan
Matthew Simpson United States
Todd Plantenga United States
Yoichi Iwata Japan
Cong Fu China
Cheng Fu relative to Tingsong Jiang China Tingsong Jiang's profile →
Citations per field
00.5×3.9×
Tingsong Jiang · 1×
Citations per year

Countries citing papers authored by Cheng Fu

Since Specialization
Citations

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

Fields of papers citing papers by Cheng Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cheng Fu

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 2
2 3
3
N-Bref : A High-fidelity Decompiler Exploiting Programming Structures
1
4 1
5 1
6 34
7
Coda: An End-to-End Neural Program Decompiler
7
8 27
9 163
10 44
11 100
12 3
13 3
14 10
15 12
16 1
17 6
18 9
19 1
20 0

About Cheng Fu

Cheng Fu is a scholar working on Hardware and Architecture, Artificial Intelligence and Signal Processing, having authored 21 papers that have together received 487 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (7 papers), Physical Unclonable Functions (PUFs) and Hardware Security (5 papers) and Advanced Neural Network Applications (3 papers). The work is most often cited by research in Artificial Intelligence (339 citations), Hardware and Architecture (65 citations) and Signal Processing (82 citations). Cheng Fu has collaborated with scholars based in United States, China and Israel. Frequent co-authors include Jishen Zhao, Huili Chen, Farinaz Koushanfar, Bita Darvish Rouhani, Le Sun, Xianpei Han, David D. Wentzloff, Sara A. Pozzi, Angela Di Fulvio and Shaun D. Clarke. Their work appears in journals such as Energies, Annals of Nuclear Energy and Processes.

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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