Mingi Ji

593 total citations
11 papers, 334 citations indexed

About

Mingi Ji is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Mingi Ji has authored 11 papers receiving a total of 334 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 3 papers in Information Systems. Recurrent topics in Mingi Ji's work include Topic Modeling (3 papers), Recommender Systems and Techniques (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). Mingi Ji is often cited by papers focused on Topic Modeling (3 papers), Recommender Systems and Techniques (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). Mingi Ji collaborates with scholars based in South Korea, Canada and United States. Mingi Ji's co-authors include Il‐Chul Moon, Byeongho Heo, Seungjae Shin, Gibeom Park, Teakgyu Hong, Wonseok Hwang, Dae‐Hyun Nam, Kyungwoo Song, Jinkyoo Park and Seung-won Hwang and has published in prestigious journals such as Proceedings of the AAAI Conference on Artificial Intelligence and Journal of Korean Institute of Industrial Engineers.

In The Last Decade

Mingi Ji

10 papers receiving 325 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mingi Ji South Korea	7	189	188	49	25	19	11	334
Jian Cao China	11	166 0.9×	206 1.1×	46 0.9×	31 1.2×	40 2.1×	42	401
Litao Yu Australia	8	174 0.9×	262 1.4×	31 0.6×	18 0.7×	38 2.0×	27	399
Baoyun Peng China	4	264 1.4×	308 1.6×	33 0.7×	25 1.0×	17 0.9×	8	445
Chih‐Kuan Yeh United States	7	348 1.8×	158 0.8×	53 1.1×	33 1.3×	9 0.5×	9	436
Fuad M. Alkoot Kuwait	8	161 0.9×	156 0.8×	39 0.8×	81 3.2×	18 0.9×	21	329
Haoyu Zhang China	7	219 1.2×	175 0.9×	23 0.5×	50 2.0×	16 0.8×	20	369
Şerafettin Taşcı Türkiye	6	278 1.5×	191 1.0×	40 0.8×	16 0.6×	18 0.9×	7	347
Haoli Bai China	10	229 1.2×	163 0.9×	21 0.4×	18 0.7×	16 0.8×	26	334
Rong Xiao China	11	225 1.2×	220 1.2×	92 1.9×	32 1.3×	19 1.0×	19	420

Countries citing papers authored by Mingi Ji

Since Specialization

Citations

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

Fields of papers citing papers by Mingi Ji

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mingi Ji

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

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Hwang, Seung-won, et al.. (2024). Breaking ReLU Barrier: Generalized MoEfication for Dense Pretrained Models. 10097–10107.

2.

Kim, Sungeun, et al.. (2023). Welfare Program Recommendation by Conditional Variational Autoencoder and Collaborative Filtering. Journal of Korean Institute of Industrial Engineers. 49(1). 28–36. 1 indexed citations

3.

Ji, Mingi, et al.. (2022). Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation. 16755–16767. 1 indexed citations

4.

Hong, Teakgyu, et al.. (2022). BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents. Proceedings of the AAAI Conference on Artificial Intelligence. 36(10). 10767–10775. 58 indexed citations

5.

Ji, Mingi, et al.. (2021). Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation. 10659–10668. 113 indexed citations

6.

Hong, Teakgyu, et al.. (2021). BROS: A Pre-trained Language Model for Understanding Texts in Document. 15 indexed citations

7.

Ji, Mingi, et al.. (2021). Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching. Proceedings of the AAAI Conference on Artificial Intelligence. 35(9). 7945–7952. 93 indexed citations

8.

Ji, Mingi, et al.. (2020). Sequential Recommendation with Relation-Aware Kernelized Self-Attention. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 4304–4311. 21 indexed citations

9.

Song, Kyungwoo, et al.. (2019). Hierarchical Context Enabled Recurrent Neural Network for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 4983–4991. 23 indexed citations

10.

Song, Kyungwoo, et al.. (2019). Adversarial Dropout for Recurrent Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 4699–4706. 2 indexed citations

11.

Ji, Mingi, et al.. (2018). Deep Reinforcement Learning with Fully Convolutional Neural Network to Solve an Earthwork Scheduling Problem. 4236–4242. 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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