Minhyeok Lee

2.1k total citations · 1 hit paper
74 papers, 1.1k citations indexed

About

Minhyeok Lee is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Minhyeok Lee has authored 74 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Computer Vision and Pattern Recognition, 24 papers in Artificial Intelligence and 14 papers in Molecular Biology. Recurrent topics in Minhyeok Lee's work include Generative Adversarial Networks and Image Synthesis (13 papers), Gene expression and cancer classification (9 papers) and Anomaly Detection Techniques and Applications (7 papers). Minhyeok Lee is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (13 papers), Gene expression and cancer classification (9 papers) and Anomaly Detection Techniques and Applications (7 papers). Minhyeok Lee collaborates with scholars based in South Korea, United States and United Kingdom. Minhyeok Lee's co-authors include Junhee Seok, Sangyoun Lee, Dogyoon Lee, Suhwan Cho, Chaewon Park, Seunghoon Lee, In-Soo Kim, Jae Sang Heo, Seong Hyun Kim and Jee Young Kwak and has published in prestigious journals such as Advanced Materials, Bioinformatics and PLoS ONE.

In The Last Decade

Minhyeok Lee

67 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition

2023 131 citations Minhyeok Lee et al. profile →

Peers

Minhyeok Lee

CVPR

AI

BE

MB

RNMI

Xinyu Tian China

Lian Li China

Issei Sato Japan

Hairong Lv China

Zhihong Zhang China

Xiyu Liu China

Fenglin Liu China

Quan Liu China

Qihang Zhang China

Mohammad Azzeh Jordan

Xinyu Tian China

Minhyeok Lee

290 ×0.7

CVPR

281 ×0.9

AI

185 ×0.9

BE

160 ×0.8

MB

93 ×1.1

RNMI

Citations per year, relative to Minhyeok Lee Minhyeok Lee (= 1×) peers Xinyu Tian

Countries citing papers authored by Minhyeok Lee

Since Specialization

Citations

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

Fields of papers citing papers by Minhyeok Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Minhyeok Lee

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

All Works

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20 of 20 papers shown

1.

Lee, Minhyeok, et al.. (2024). Box-Counting Dimension Sequences of Level Sets in AI-Generated Fractals. Fractal and Fractional. 8(12). 730–730. 2 indexed citations

2.

Lee, Minhyeok, et al.. (2024). Persistent Homology Analysis of AI-Generated Fractal Patterns: A Mathematical Framework for Evaluating Geometric Authenticity. Fractal and Fractional. 8(12). 731–731.

3.

Lee, Minhyeok, et al.. (2024). Stabilized GAN models training with kernel-histogram transformation and probability mass function distance. Applied Soft Computing. 164. 112003–112003. 6 indexed citations

4.

Lee, Minhyeok. (2024). Fractal Self-Similarity in Semantic Convergence: Gradient of Embedding Similarity across Transformer Layers. Fractal and Fractional. 8(10). 552–552. 3 indexed citations

5.

Lee, Minhyeok, et al.. (2024). Deep Transfer Learning Using Real-World Image Features for Medical Image Classification, with a Case Study on Pneumonia X-ray Images. Bioengineering. 11(4). 406–406. 9 indexed citations

6.

Lee, Minhyeok. (2023). Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis. Bioengineering. 10(8). 897–897. 10 indexed citations

7.

Lee, Minhyeok. (2023). A Mathematical Interpretation of Autoregressive Generative Pre-Trained Transformer and Self-Supervised Learning. Mathematics. 11(11). 2451–2451. 17 indexed citations

8.

Lee, Minhyeok, et al.. (2023). Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments. International Journal of Molecular Sciences. 24(12). 10299–10299. 12 indexed citations

9.

Lee, Minhyeok. (2023). A Mathematical Investigation of Hallucination and Creativity in GPT Models. Mathematics. 11(10). 2320–2320. 52 indexed citations

10.

Lee, Minhyeok. (2023). The Geometry of Feature Space in Deep Learning Models: A Holistic Perspective and Comprehensive Review. Mathematics. 11(10). 2375–2375. 7 indexed citations

11.

Lee, Minhyeok. (2023). Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review. Molecules. 28(13). 5169–5169. 39 indexed citations

12.

Lee, Minhyeok, et al.. (2023). BioEdge: Accelerating Object Detection in Bioimages with Edge-Based Distributed Inference. Electronics. 12(21). 4544–4544. 1 indexed citations

13.

Lee, Minhyeok, et al.. (2023). Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review. Biology. 12(7). 1033–1033. 94 indexed citations

14.

Lee, Minhyeok. (2023). Mathematical Analysis and Performance Evaluation of the GELU Activation Function in Deep Learning. Journal of Mathematics. 2023. 1–13. 53 indexed citations

15.

Lee, Minhyeok, et al.. (2023). ICEGAN: inverse covariance estimating generative adversarial network. Machine Learning Science and Technology. 4(2). 25008–25008. 13 indexed citations

16.

Lee, Minhyeok, et al.. (2023). TextControlGAN: Text-to-Image Synthesis with Controllable Generative Adversarial Networks. Applied Sciences. 13(8). 5098–5098. 20 indexed citations

17.

Lee, Minhyeok, et al.. (2023). Deep learning model with L1 penalty for predicting breast cancer metastasis using gene expression data. Machine Learning Science and Technology. 4(2). 25026–25026. 6 indexed citations

18.

Lee, Minhyeok & Junhee Seok. (2022). Score-Guided Generative Adversarial Networks. Axioms. 11(12). 701–701. 11 indexed citations

19.

Lee, Minhyeok, et al.. (2022). HRGAN: A Generative Adversarial Network Producing Higher-Resolution Images than Training Sets. Sensors. 22(4). 1435–1435. 9 indexed citations

20.

Lee, Minhyeok & Junhee Seok. (2021). Estimation with Uncertainty via Conditional Generative Adversarial Networks. Sensors. 21(18). 6194–6194. 13 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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