Dae‐Shik Kim

2.7k total citations
74 papers, 1.7k citations indexed

About

Dae‐Shik Kim is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Dae‐Shik Kim has authored 74 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Cognitive Neuroscience, 19 papers in Computer Vision and Pattern Recognition and 16 papers in Artificial Intelligence. Recurrent topics in Dae‐Shik Kim's work include Visual perception and processing mechanisms (13 papers), Neural dynamics and brain function (12 papers) and Functional Brain Connectivity Studies (10 papers). Dae‐Shik Kim is often cited by papers focused on Visual perception and processing mechanisms (13 papers), Neural dynamics and brain function (12 papers) and Functional Brain Connectivity Studies (10 papers). Dae‐Shik Kim collaborates with scholars based in South Korea, United States and Germany. Dae‐Shik Kim's co-authors include Tobias Bonhoeffer, Lief E. Fenno, Jin Hyung Lee, Viviana Gradinaru, Rémy Durand, Inbal Goshen, Charu Ramakrishnan, Karl Deisseroth, Feng Zhang and Wolf Singer and has published in prestigious journals such as Nature, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

Dae‐Shik Kim

65 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dae‐Shik Kim South Korea 19 1.1k 686 238 148 134 74 1.7k
Angelo Arleo France 21 1.0k 0.9× 787 1.1× 105 0.4× 314 2.1× 129 1.0× 81 1.8k
Kechen Zhang United States 19 1.8k 1.7× 1.0k 1.5× 152 0.6× 184 1.2× 69 0.5× 45 2.3k
Naotaka Fujii Japan 31 2.3k 2.1× 840 1.2× 151 0.6× 139 0.9× 119 0.9× 57 2.9k
Aniruddha Das United States 17 1.9k 1.8× 703 1.0× 266 1.1× 239 1.6× 50 0.4× 28 2.2k
Frédéric Chavane France 20 1.9k 1.7× 1.0k 1.5× 201 0.8× 170 1.1× 70 0.5× 59 2.2k
Fred H. Hamker Germany 27 1.5k 1.4× 340 0.5× 69 0.3× 133 0.9× 100 0.7× 108 2.1k
George Zouridakis United States 33 2.0k 1.9× 276 0.4× 325 1.4× 116 0.8× 198 1.5× 113 2.9k
Joseph T. Francis United States 21 1.4k 1.3× 623 0.9× 77 0.3× 131 0.9× 377 2.8× 79 2.0k
Hamutal Slovin Israel 18 1.9k 1.8× 1.7k 2.4× 184 0.8× 158 1.1× 85 0.6× 37 2.8k
Peter Janssen Belgium 29 2.6k 2.5× 476 0.7× 121 0.5× 185 1.3× 108 0.8× 90 3.2k

Countries citing papers authored by Dae‐Shik Kim

Since Specialization
Citations

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

Fields of papers citing papers by Dae‐Shik Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dae‐Shik Kim. 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 Dae‐Shik Kim. The network helps show where Dae‐Shik Kim may publish in the future.

Co-authorship network of co-authors of Dae‐Shik Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Dae‐Shik Kim. A scholar is included among the top collaborators of Dae‐Shik Kim 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 Dae‐Shik Kim. Dae‐Shik Kim 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
1.
Kim, Dae‐Shik, et al.. (2024). Maximizing discrimination capability of knowledge distillation with energy function. Knowledge-Based Systems. 296. 111911–111911. 5 indexed citations
2.
Kim, Dae‐Shik, et al.. (2024). Difficulty level-based knowledge distillation. Neurocomputing. 606. 128375–128375. 1 indexed citations
3.
Kim, Dae‐Shik, et al.. (2024). Adaptive class token knowledge distillation for efficient vision transformer. Knowledge-Based Systems. 304. 112531–112531. 1 indexed citations
4.
Shin, Mincheol, et al.. (2024). SemiH: DFT Hamiltonian neural network training with semi-supervised learning. Machine Learning Science and Technology. 5(3). 35060–35060.
5.
Lee, Jungsoo, et al.. (2024). Comparing effects of wearable robot-assisted gait training on functional changes and neuroplasticity: A preliminary study. PLoS ONE. 19(12). e0315145–e0315145. 1 indexed citations
6.
Kim, Dae‐Shik, et al.. (2022). Learning Color Representations for Low-Light Image Enhancement. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 904–912. 18 indexed citations
7.
Kim, Minseon, et al.. (2019). Progressive Face Super-Resolution via Attention to Facial Landmark.. British Machine Vision Conference. 192. 3 indexed citations
8.
Kim, Dae‐Shik, et al.. (2018). Latent Question Interpretation Through Parameter Adaptation Using Stochastic Neuron. International Joint Conference on Artificial Intelligence. 46–55. 1 indexed citations
9.
Lee, Jungsoo, Eunhee Park, Ahee Lee, et al.. (2018). Alteration and Role of Interhemispheric and Intrahemispheric Connectivity in Motor Network After Stroke. Brain Topography. 31(4). 708–719. 37 indexed citations
10.
Leeb, Robert, et al.. (2013). Freeing the visual channel by exploiting vibrotactile BCI feedback. PubMed. 2013. 3093–3096. 26 indexed citations
11.
Kim, Dae‐Shik, et al.. (2013). Pattern-Based Granger Causality Mapping in fMRI. Brain Connectivity. 3(6). 569–577. 8 indexed citations
12.
Leeb, Robert, et al.. (2013). Feel the BCI vibe – vibrotactile BCI feedback. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1 indexed citations
13.
Lee, Jin Hyung, Rémy Durand, Viviana Gradinaru, et al.. (2010). Global and local fMRI signals driven by neurons defined optogenetically by type and wiring. Nature. 465(7299). 788–792. 490 indexed citations
14.
Koo, Bang‐Bon, Kiri Choi, Itamar Ronen, Jong‐Min Lee, & Dae‐Shik Kim. (2010). Quantitative mapping of diffusion characteristics under the cortical surface. Magnetic Resonance Imaging. 28(8). 1175–1182. 2 indexed citations
15.
Vladusich, Tony, et al.. (2010). Prototypical category learning in high‐functioning autism. Autism Research. 3(5). 226–236. 47 indexed citations
16.
Matsuda, Yoshitaka, et al.. (2000). Coincidence of ipsilateral ocular dominance peaks with orientation pinwheel centers in cat visual cortex. Neuroreport. 11(15). 3337–3343. 5 indexed citations
17.
Löwel, Siegrid, Kerstin Schmidt, Dae‐Shik Kim, et al.. (1998). The layout of orientation and ocular dominance domains in area 17 of strabismic cats. European Journal of Neuroscience. 10(8). 2629–2643. 43 indexed citations
18.
Nagurney, Anna & Dae‐Shik Kim. (1991). Parallel computation of large-scale dynamic market network equilibria via time period decomposition. Mathematical and Computer Modelling. 15(6). 55–67. 8 indexed citations
19.
Nagurney, Anna, Alexander Eydeland, & Dae‐Shik Kim. (1990). Computation of large-scale constrained matrix problems: the splitting equilibration algorithm. Conference on High Performance Computing (Supercomputing). 214–223. 3 indexed citations
20.
Kim, Dae‐Shik, et al.. (1979). Physical Education and Sport in the Soviet Union.. The Physical Educator. 36(1). 39–44.

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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