Jinmook Lee

1.8k citations
28 papers · 1.3k · h-index 16

Impact in

Papers in

Jinmook Lee

28 papers receiving 1.3k citations

Peers

Jinmook Lee
Comparison fields: 5 of 64
  • Computer Vision and Pattern Recognition 754
  • Computational Mathematics 18
  • Hardware and Architecture 168
  • Electrical and Electronic Engineering 888
  • Artificial Intelligence 407
Replace Fengbin Tu with:
Fengbin Tu China
Sanghoon Kang South Korea
Liqiang He China
Peng Ouyang China
Boxun Li China
Donghyeon Han South Korea
Yijin Guan China
Hiroki Nakahara Japan
Naveen Suda United States
Jinmook Lee relative to Fengbin Tu China Fengbin Tu's profile →
Citations per field
00.5×6.3×
Fengbin Tu · 1×
Citations per year

Countries citing papers authored by Jinmook Lee

Since Specialization
Citations

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

Fields of papers citing papers by Jinmook Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside Jinmook Lee, 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 Jinmook Lee Line = papers co-authored together Jinmook Lee links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018242
2 2017240
3 2018236
4 2019117
5 201578
6 201847
7 202039
8 201634
9
A 1.93 TOPS/W Scalable Deep Learning/Inference Processor with Tetra-parallel MIMD Architecture for Big Data Applications
201534
10 201833
11 201929
12 201925
13 202024
14 201622
15 201920
16 201820
17 201714
18 202013
19 202012
20 201811

About Jinmook Lee

Jinmook Lee is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Signal Processing, Artificial Intelligence and Computational Mechanics, having authored 28 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (14 papers), Advanced Memory and Neural Computing (10 papers), CCD and CMOS Imaging Sensors (7 papers), Ferroelectric and Negative Capacitance Devices (5 papers), Speech and Audio Processing (5 papers), Advanced Image and Video Retrieval Techniques (4 papers), Multimodal Machine Learning Applications (3 papers) and Face and Expression Recognition (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (754 citations), Computational Mathematics (18 citations), Hardware and Architecture (168 citations), Electrical and Electronic Engineering (888 citations) and Artificial Intelligence (407 citations). Jinmook Lee has collaborated with scholars based in South Korea and Canada. Frequent co-authors include Hoi‐Jun Yoo, Dongjoo Shin, Jinsu Lee, Sanghoon Kang, Changhyeon Kim, Sangyeob Kim, Donghyeon Han, Juhyoung Lee, Sungpill Choi and Seong‐Wook Park. Their work appears in journals such as IEEE Transactions on Circuits and Systems I Regular Papers, IEEE Solid-State Circuits Letters, IEEE Micro, IEEE Journal on Emerging and Selected Topics in Circuits and Systems and IEEE Transactions on Circuits & Systems II Express Briefs.

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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