Chulhee Yun

574 total citations
13 papers, 108 citations indexed

About

Chulhee Yun is a scholar working on Artificial Intelligence, Computational Mechanics and Electrical and Electronic Engineering. According to data from OpenAlex, Chulhee Yun has authored 13 papers receiving a total of 108 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 5 papers in Computational Mechanics and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Chulhee Yun's work include Stochastic Gradient Optimization Techniques (8 papers), Sparse and Compressive Sensing Techniques (5 papers) and Complexity and Algorithms in Graphs (2 papers). Chulhee Yun is often cited by papers focused on Stochastic Gradient Optimization Techniques (8 papers), Sparse and Compressive Sensing Techniques (5 papers) and Complexity and Algorithms in Graphs (2 papers). Chulhee Yun collaborates with scholars based in United States and South Korea. Chulhee Yun's co-authors include Suvrit Sra, Ali Jadbabaie, Myoung Hwan Kim, Sang‐Hyug Park, Junghwan Oh, Elna Paul Chalisserry, Seung Yun Nam, Yong Wook Lee, Hyun Wook Kang and Won‐Kyo Jung and has published in prestigious journals such as Materials Letters, arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Chulhee Yun

12 papers receiving 105 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chulhee Yun United States	6	46	46	29	16	16	13	108
Anusha Nagabandi United States	4	8 0.2×	25 0.5×	5 0.2×	8 0.5×	5 0.3×	6	54
Qirui Yang China	6	7 0.2×	12 0.3×	29 1.0×	22 1.4×		16	107
Xiaoxia Wu United States	3	7 0.2×	33 0.7×	6 0.2×	14 0.9×	1 0.1×	5	69
Romain Gay United States	4	6 0.1×	30 0.7×	17 0.6×	5 0.3×	1 0.1×	6	88
Yifu Zhang United States	3	18 0.4×	13 0.3×	6 0.2×	105 6.6×		5	113
Jiajun Zheng China	5	28 0.6×	7 0.2×	4 0.1×	10 0.6×		9	107
Harsh Agrawal India	7	4 0.1×	45 1.0×	7 0.2×	22 1.4×		17	125
Alfredo Canziani Switzerland	4	8 0.2×	14 0.3×	8 0.3×	16 1.0×	1 0.1×	4	50
Shaowei Li China	3	10 0.2×	13 0.3×	2 0.1×	13 0.8×		5	55

Countries citing papers authored by Chulhee Yun

Since Specialization

Citations

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

Fields of papers citing papers by Chulhee Yun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chulhee Yun

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

All Works

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13 of 13 papers shown

Yun, Chulhee, et al.. (2024). Provable Benefit of Cutout and CutMix for Feature Learning. 114656–114743.

Yun, Chulhee, Suvrit Sra, & Ali Jadbabaie. (2021). Open Problem: Can Single-Shuffle SGD be Better than Reshuffling SGD and GD?. Conference on Learning Theory. 4653–4658. 1 indexed citations

Yun, Chulhee, Shankar Krishnan, & Hossein Mobahi. (2021). A unifying view on implicit bias in training linear neural networks. International Conference on Learning Representations. 2 indexed citations

Park, Sejun, Chulhee Yun, Jaeho Lee, & Jinwoo Shin. (2021). Minimum Width for Universal Approximation. 14 indexed citations

Ahn, Kwangjun, Chulhee Yun, & Suvrit Sra. (2020). SGD with shuffling: optimal rates without component convexity and large epoch requirements. Neural Information Processing Systems. 33. 17526–17535. 2 indexed citations

Yun, Chulhee, Suvrit Sra, & Ali Jadbabaie. (2019). Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity. DSpace@MIT (Massachusetts Institute of Technology). 32. 15532–15543. 9 indexed citations

Yun, Chulhee, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, & Sanjiv Kumar. (2019). Are Transformers universal approximators of sequence-to-sequence functions?. arXiv (Cornell University). 10 indexed citations

Yun, Chulhee, Suvrit Sra, & Ali Jadbabaie. (2018). A Critical View of Global Optimality in Deep Learning. arXiv (Cornell University). 10 indexed citations

Duchi, John C., Feng Ruan, & Chulhee Yun. (2018). Minimax Bounds on Stochastic Batched Convex Optimization. Conference on Learning Theory. 3065–3162. 3 indexed citations

10.

Yun, Chulhee, Suvrit Sra, & Ali Jadbabaie. (2018). Efficiently testing local optimality and escaping saddles for ReLU networks. arXiv (Cornell University). 3 indexed citations

11.

Yun, Chulhee, Suvrit Sra, & Ali Jadbabaie. (2018). Small nonlinearities in activation functions create bad local minima in neural networks. arXiv (Cornell University). 5 indexed citations

12.

Kim, Myoung Hwan, Chulhee Yun, Elna Paul Chalisserry, et al.. (2018). Quantitative analysis of the role of nanohydroxyapatite (nHA) on 3D-printed PCL/nHA composite scaffolds. Materials Letters. 220. 112–115. 48 indexed citations

13.

Yun, Chulhee, et al.. (2013). A fusion of computer vision technique and a visual programming language for edutainment robots. 1. 1–5. 1 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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