Shaokai Ye

2.0k total citations · 2 hit papers
9 papers, 538 citations indexed

About

Shaokai Ye is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Shaokai Ye has authored 9 papers receiving a total of 538 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 2 papers in Computational Mechanics. Recurrent topics in Shaokai Ye's work include Advanced Neural Network Applications (4 papers), Adversarial Robustness in Machine Learning (3 papers) and Sparse and Compressive Sensing Techniques (2 papers). Shaokai Ye is often cited by papers focused on Advanced Neural Network Applications (4 papers), Adversarial Robustness in Machine Learning (3 papers) and Sparse and Compressive Sensing Techniques (2 papers). Shaokai Ye collaborates with scholars based in United States, Switzerland and China. Shaokai Ye's co-authors include Alexander Mathis, Mackenzie Weygandt Mathis, Xue Lin, Steffen Schneider, Jessy Lauer, Yanzhi Wang, Valentina Di Santo, Tanmay Nath, Mohammed Mostafizur Rahman and William Menegas and has published in prestigious journals such as Nature Communications, Nature Methods and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Shaokai Ye

8 papers receiving 525 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.

Multi-animal pose estimation, identification and tracking with DeepLabCut

2022 264 citations Jessy Lauer, Mu Zhou et al. Nature Methods profile →
Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

2024 48 citations Caleb Weinreb, Sherry Lin et al. Nature Methods profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shaokai Ye United States	7	198	145	71	61	60	9	538
Daniel H. Chae Australia	5	110 0.6×	59 0.4×	56 0.8×	58 1.0×	18 0.3×	9	460
Mu Zhou China	11	102 0.5×	109 0.8×	62 0.9×	49 0.8×	66 1.1×	48	748
Steffen Schneider Germany	8	121 0.6×	105 0.7×	99 1.4×	148 2.4×	10 0.2×	17	680
Hai Nguyen United States	19	230 1.2×	128 0.9×	108 1.5×	83 1.4×	53 0.9×	41	1.1k
Malte Schilling Germany	14	52 0.3×	82 0.6×	83 1.2×	137 2.2×	22 0.4×	46	641
Shiv Vitaladevuni United States	17	344 1.7×	368 2.5×	25 0.4×	210 3.4×	57 0.9×	29	1.0k
Ulysses Bernardet Spain	14	95 0.5×	53 0.4×	114 1.6×	187 3.1×	29 0.5×	48	537
Si Wu China	8	258 1.3×	300 2.1×	54 0.8×	107 1.8×	49 0.8×	33	668
Mayank Kabra India	7	93 0.5×	101 0.7×	103 1.5×	356 5.8×	25 0.4×	20	840
Hugo Gravato Marques Switzerland	12	47 0.2×	55 0.4×	100 1.4×	293 4.8×	19 0.3×	22	716

Countries citing papers authored by Shaokai Ye

Since Specialization

Citations

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

Fields of papers citing papers by Shaokai Ye

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shaokai Ye

This figure shows the co-authorship network connecting the top 25 collaborators of Shaokai Ye. A scholar is included among the top collaborators of Shaokai Ye 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 Shaokai Ye. Shaokai Ye 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:

9 of 9 papers shown

1.

Weinreb, Caleb, Sherry Lin, Mohammed Abdal Monium Osman, et al.. (2024). Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics. Nature Methods. 21(7). 1329–1339. 48 indexed citations breakdown →

2.

Ye, Shaokai, Jessy Lauer, Steffen Schneider, et al.. (2024). SuperAnimal pretrained pose estimation models for behavioral analysis. Nature Communications. 15(1). 5165–5165. 28 indexed citations

3.

Lauer, Jessy, Mu Zhou, Shaokai Ye, et al.. (2022). Multi-animal pose estimation, identification and tracking with DeepLabCut. Nature Methods. 19(4). 496–504. 264 indexed citations breakdown →

4.

Ma, Xiaolong, Sheng Lin, Shaokai Ye, et al.. (2021). Non-Structured DNN Weight Pruning—Is It Beneficial in Any Platform?. IEEE Transactions on Neural Networks and Learning Systems. 33(9). 4930–4944. 55 indexed citations

5.

Lu, Bo, Shaokai Ye, & Wei Wang. (2021). Research on AGC Optimal Reference Power in Broadband PLC Multipath Channel. 2021 IEEE 21st International Conference on Communication Technology (ICCT). 63. 285–288.

6.

Wang, Yanzhi, Shaokai Ye, Zhezhi He, et al.. (2019). Non-structured DNN Weight Pruning Considered Harmful. 5 indexed citations

7.

Ren, Ao, Tianyun Zhang, Shaokai Ye, et al.. (2019). ADMM-NN. 925–938. 105 indexed citations

8.

Li, Hongjia, Ning Liu, Xiaolong Ma, et al.. (2019). ADMM-based Weight Pruning for Real-Time Deep Learning Acceleration on Mobile Devices. 501–506. 17 indexed citations

9.

Wang, Xiao, et al.. (2018). Defending DNN Adversarial Attacks with Pruning and Logits Augmentation. 1144–1148. 16 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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