Joonsang Yu

417 total citations
16 papers, 275 citations indexed

About

Joonsang Yu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Joonsang Yu has authored 16 papers receiving a total of 275 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 6 papers in Electrical and Electronic Engineering. Recurrent topics in Joonsang Yu's work include Advanced Neural Network Applications (9 papers), Neural Networks and Applications (4 papers) and Advanced Memory and Neural Computing (4 papers). Joonsang Yu is often cited by papers focused on Advanced Neural Network Applications (9 papers), Neural Networks and Applications (4 papers) and Advanced Memory and Neural Computing (4 papers). Joonsang Yu collaborates with scholars based in South Korea and Canada. Joonsang Yu's co-authors include Ki‐Young Choi, Jongeun Lee, Jungki Kim, Dong Hyun Lee, Jungwook Choi, Seongmin Park, Noseong Park, Jinho Lee, Youngjoon Yoo and Youngsok Kim and has published in prestigious journals such as Journal of Signal Processing Systems, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Scholarworks@UNIST (Ulsan National Institute of Science and Technology).

In The Last Decade

Joonsang Yu

16 papers receiving 272 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Joonsang Yu South Korea	7	162	156	143	78	23	16	275
Hyeonuk Sim South Korea	10	181 1.1×	187 1.2×	195 1.4×	78 1.0×	24 1.0×	19	311
Wenqing Song China	11	99 0.6×	250 1.6×	239 1.7×	20 0.3×	20 0.9×	34	343
Youngeun Kwon South Korea	8	119 0.7×	115 0.7×	132 0.9×	86 1.1×	96 4.2×	8	309
Coleman Hooper United States	6	92 0.6×	92 0.6×	41 0.3×	52 0.7×	46 2.0×	11	215
Houxiang Ji United States	7	91 0.6×	81 0.5×	60 0.4×	56 0.7×	56 2.4×	15	181
Linyan Mei Belgium	9	66 0.4×	187 1.2×	53 0.4×	122 1.6×	83 3.6×	17	300
Smita Paira India	8	99 0.6×	114 0.7×	87 0.6×	83 1.1×	10 0.4×	16	278
Naoya Torii Japan	6	222 1.4×	40 0.3×	84 0.6×	82 1.1×	48 2.1×	17	297
Yiqun Ge Canada	7	73 0.5×	114 0.7×	115 0.8×	29 0.4×	8 0.3×	23	210
Yiyuan Luo China	9	182 1.1×	64 0.4×	50 0.3×	83 1.1×	10 0.4×	33	231

Countries citing papers authored by Joonsang Yu

Since Specialization

Citations

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

Fields of papers citing papers by Joonsang Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Joonsang Yu

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

All Works

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16 of 16 papers shown

Cho, Jungchan, et al.. (2024). Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding. Proceedings of the AAAI Conference on Artificial Intelligence. 38(3). 2795–2803. 7 indexed citations

Kim, Beomyoung, et al.. (2024). EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection. 977–987. 5 indexed citations

Yu, Joonsang, et al.. (2024). ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning. 3346–3356. 3 indexed citations

Jung, Jaewon, et al.. (2023). Pipe-BD: Pipelined Parallel Blockwise Distillation. Seoul National University Open Repository (Seoul National University). 1–6. 1 indexed citations

Yu, Joonsang, et al.. (2023). GeNAS: Neural Architecture Search with Better Generalization. 911–919. 2 indexed citations

Yu, Joonsang, et al.. (2022). NN-LUT. Proceedings of the 59th ACM/IEEE Design Automation Conference. 577–582. 32 indexed citations

Yu, Joonsang, et al.. (2022). It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 8301–8311. 21 indexed citations

Yu, Joonsang, et al.. (2022). Enabling hard constraints in differentiable neural network and accelerator co-exploration. Proceedings of the 59th ACM/IEEE Design Automation Conference. 589–594. 2 indexed citations

Lee, Gunhee, et al.. (2019). Acceleration of DNN Backward Propagation by Selective Computation of Gradients. 1–6. 9 indexed citations

10.

Yu, Joonsang, Sung-Bum Kang, & Ki‐Young Choi. (2019). Network Recasting: A Universal Method for Network Architecture Transformation. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 5701–5708. 1 indexed citations

11.

Kang, Sung-Bum, Joonsang Yu, & Ki‐Young Choi. (2018). Tapered-Ratio Compression for Residual Network. 72–73. 1 indexed citations

12.

Yu, Joonsang, et al.. (2017). Accurate and Efficient Stochastic Computing Hardware for Convolutional Neural Networks. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 43 indexed citations

13.

Yu, Joonsang, et al.. (2017). Hybrid spiking-stochastic Deep Neural Network. 1–4. 1 indexed citations

14.

Yu, Joonsang, et al.. (2016). A new approach to binarizing neural networks. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 77–78. 1 indexed citations

15.

Kim, Jungki, et al.. (2016). Dynamic energy-accuracy trade-off using stochastic computing in deep neural networks. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 1–6. 139 indexed citations

16.

Lee, Jeong–A, et al.. (2016). Efficient Low-Cost Fault-Localization and Self-Repairing Radix-2 Signed-Digit Adders Applying the Self-Dual Concept. Journal of Signal Processing Systems. 88(3). 297–309. 7 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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