Bin Dai

419 total citations
13 papers, 100 citations indexed

About

Bin Dai is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Bin Dai has authored 13 papers receiving a total of 100 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Computer Vision and Pattern Recognition, 3 papers in Artificial Intelligence and 3 papers in Biomedical Engineering. Recurrent topics in Bin Dai's work include Generative Adversarial Networks and Image Synthesis (3 papers), Advanced ceramic materials synthesis (1 paper) and Spectroscopy and Chemometric Analyses (1 paper). Bin Dai is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (3 papers), Advanced ceramic materials synthesis (1 paper) and Spectroscopy and Chemometric Analyses (1 paper). Bin Dai collaborates with scholars based in China, United States and United Kingdom. Bin Dai's co-authors include David Wipf, Chen Zhu, Gang Hua, Baining Guo, John A. D. Aston, Yu Wang, Michael L. Myrick, Huadong Yu, Jinkai Xu and Guangjun Chen and has published in prestigious journals such as Sensors, Journal of Machine Learning Research and Ceramics International.

In The Last Decade

Bin Dai

12 papers receiving 97 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bin Dai China	5	45	39	20	20	17	13	100
Xiaoxia Wu United States	3	33 0.7×	14 0.4×	14 0.7×	16 0.8×	7 0.4×	5	69
Guanjun Wu China	4	17 0.4×	142 3.6×	16 0.8×	4 0.2×	14 0.8×	8	265
Anthony L. Caterini United Kingdom	2	34 0.8×	12 0.3×	6 0.3×	16 0.8×	11 0.6×	3	80
Amran Bhuiyan Canada	9	43 1.0×	119 3.1×	6 0.3×	21 1.1×	39 2.3×	21	187
Pawel Grzegorz Russek Poland	5	43 1.0×	35 0.9×	8 0.4×	10 0.5×	9 0.5×	28	108
Hongjie Zhang China	7	38 0.8×	33 0.8×	46 2.3×	3 0.1×	10 0.6×	27	130
Konrad Żołna Poland	5	41 0.9×	19 0.5×	10 0.5×	9 0.5×	2 0.1×	15	101
Dat Ngo Vietnam	6	36 0.8×	20 0.5×	4 0.2×	9 0.5×	8 0.5×	30	116
Luca Franceschi Italy	4	23 0.5×	32 0.8×	6 0.3×	3 0.1×	42 2.5×	10	77
Keno Fischer United States	4	12 0.3×	6 0.2×	16 0.8×	4 0.2×	13 0.8×	5	74

Countries citing papers authored by Bin Dai

Since Specialization

Citations

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

Fields of papers citing papers by Bin Dai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bin Dai

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

13 of 13 papers shown

Wang, Chenxu, Bin Dai, Huaping Liu, & Baoyuan Wang. (2024). Towards Objectively Benchmarking Social Intelligence of Language Agents at the Action Level. 8885–8897. 1 indexed citations

Zhou, Yong‐Gui, et al.. (2023). Risk assessment of passenger flow dispersion in urban rail transit transfer stations. IET conference proceedings.. 2023(9). 199–205.

Dai, Bin, et al.. (2021). On the Value of Infinite Gradients in Variational Autoencoder Models. Neural Information Processing Systems. 34. 1 indexed citations

Dai, Bin, et al.. (2021). Innovative Formation Tester Sampling Procedures for Carbon Dioxide and Other Reactive Components. Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description. 62(1). 65–72. 1 indexed citations

Yu, Huadong, et al.. (2021). Amplitude effect on micro-hole drilling of 3D needled Cf/SiC by ultrasonic vibration. Ceramics International. 47(24). 34987–35001. 15 indexed citations

Wang, Ziyu, Bin Dai, David Wipf, & Jun Zhu. (2020). Further Analysis of Outlier Detection with Deep Generative Models. Neural Information Processing Systems. 33. 8982–8992. 1 indexed citations

Dai, Bin, et al.. (2019). Formation Fluid Microsampling While Drilling: A New PVT and Geochemical Formation Evaluation Technique. SPE Annual Technical Conference and Exhibition. 2 indexed citations

Wang, Yu, Bin Dai, Gang Hua, John A. D. Aston, & David Wipf. (2018). Recurrent Variational Autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers. IEEE Journal of Selected Topics in Signal Processing. 12(6). 1615–1627. 15 indexed citations

Dai, Bin, Chen Zhu, Baining Guo, & David Wipf. (2018). Compressing Neural Networks using the Variational Information Bottleneck.. International Conference on Machine Learning. 1135–1144. 28 indexed citations

10.

Dai, Bin, Yu Wang, John A. D. Aston, Gang Hua, & David Wipf. (2018). Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models. Journal of Machine Learning Research. 19(41). 1–42. 16 indexed citations

11.

Dai, Bin, et al.. (2018). Hydrogen Sulfide Gas Detection via Multivariate Optical Computing. Sensors. 18(7). 2006–2006. 17 indexed citations

12.

Dai, Bin, et al.. (2017). Veiled Attributes of the Variational Autoencoder.. arXiv (Cornell University). 1 indexed citations

13.

Wang, Yu, Bin Dai, Gang Hua, John A. D. Aston, & David Wipf. (2017). Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders.. Uncertainty in Artificial Intelligence. 2 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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