Biye Jiang

523 total citations
8 papers, 338 citations indexed

About

Biye Jiang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Biye Jiang has authored 8 papers receiving a total of 338 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 2 papers in Information Systems. Recurrent topics in Biye Jiang's work include Generative Adversarial Networks and Image Synthesis (2 papers), Data Visualization and Analytics (2 papers) and Bayesian Methods and Mixture Models (1 paper). Biye Jiang is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (2 papers), Data Visualization and Analytics (2 papers) and Bayesian Methods and Mixture Models (1 paper). Biye Jiang collaborates with scholars based in China, United States and Hong Kong. Biye Jiang's co-authors include Jeffrey Heer, Zhicheng Liu, Kun Xu, Liqian Ma, John Canny, Shi‐Min Hu, Tien-Tsin Wong, Huasha Zhao, Bin Liu and Shuguang Han and has published in prestigious journals such as ACM Transactions on Graphics, IEEE Transactions on Visualization and Computer Graphics and Computer Graphics Forum.

In The Last Decade

Biye Jiang

8 papers receiving 323 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Biye Jiang China	7	231	124	82	79	55	8	338
Jörn Schneidewind Germany	9	289 1.3×	104 0.8×	111 1.4×	37 0.5×	31 0.6×	16	365
Stef van den Elzen Netherlands	9	441 1.9×	113 0.9×	209 2.5×	23 0.3×	33 0.6×	16	550
Xuedi Qin China	9	332 1.4×	109 0.9×	297 3.6×	66 0.8×	125 2.3×	15	586
Bruno Pinaud France	8	250 1.1×	57 0.5×	90 1.1×	35 0.4×	34 0.6×	28	358
Emanuel Zgraggen United States	13	305 1.3×	179 1.4×	274 3.3×	120 1.5×	93 1.7×	22	541
Mei C. Chuah United States	11	305 1.3×	79 0.6×	117 1.4×	50 0.6×	70 1.3×	17	409
John Kolojejchick United States	8	325 1.4×	108 0.9×	115 1.4×	88 1.1×	56 1.0×	13	396
Wolfgang Kienreich Austria	10	224 1.0×	66 0.5×	119 1.5×	15 0.2×	73 1.3×	28	321
Lisa Tweedie United Kingdom	8	268 1.2×	80 0.6×	100 1.2×	39 0.5×	39 0.7×	13	347
William Hendrix United States	9	65 0.3×	45 0.4×	168 2.0×	142 1.8×	51 0.9×	27	363

Countries citing papers authored by Biye Jiang

Since Specialization

Citations

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

Fields of papers citing papers by Biye Jiang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Biye Jiang

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

All Works

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8 of 8 papers shown

1.

Corizzo, Roberto, Junfeng Ge, Colin Bellinger, et al.. (2022). 4th Workshop on Deep Learning Practice and Theory for High-Dimensional Sparse and Imbalanced Data with KDD 2022. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4860–4861. 1 indexed citations

2.

Sheng, Xiang-Rong, et al.. (2022). Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Models. Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2671–2680. 21 indexed citations

3.

Jiang, Biye, Chao Deng, Huimin Yi, et al.. (2019). XDL. 1–9. 34 indexed citations

4.

Jiang, Biye & John Canny. (2017). Interactive Machine Learning via a GPU-accelerated Toolkit. 535–546. 8 indexed citations

5.

Zhao, Huasha, et al.. (2015). SAME but Different. 1495–1502. 14 indexed citations

6.

Ma, Liqian, Kun Xu, Tien-Tsin Wong, Biye Jiang, & Shi‐Min Hu. (2013). Change Blindness Images. IEEE Transactions on Visualization and Computer Graphics. 19(11). 1808–1819. 25 indexed citations

7.

Hu, Shi‐Min, Kun Xu, Liqian Ma, et al.. (2013). Inverse image editing. ACM Transactions on Graphics. 32(6). 1–11. 17 indexed citations

8.

Liu, Zhicheng, Biye Jiang, & Jeffrey Heer. (2013). imMens: Real‐time Visual Querying of Big Data. Computer Graphics Forum. 32(3pt4). 421–430. 218 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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