Bin Dai
Impact in
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- Advanced Neural Network Applications
- Generative Adversarial Networks and Image Synthesis
Papers in
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- Generative Adversarial Networks and Image Synthesis 3
- Image Processing and 3D Reconstruction 1
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- Natural Language Processing Techniques 1
- Co-authors
- David Wipf (7 shared papers)Gang Hua (3 shared papers)Baining Guo (1 shared paper)Chen Zhu (1 shared paper)John A. D. Aston (4 shared papers)Michael L. Myrick (1 shared paper)Jinkai Xu (1 shared paper)Yu Wang (1 shared paper)
- Journals
- IEEE Journal of Selected Topics in Signal Processing (1 paper)Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description (1 paper)Ceramics International (1 paper)Journal of Machine Learning Research (1 paper)Sensors (1 paper)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Bin Dai
12 papers receiving 97 citations
Peers
Comparison fields: 5 of 43
- Computer Vision and Pattern Recognition 39
- Computational Mathematics 1
- Artificial Intelligence 45
- Fuel Technology 1
- Ceramics and Composites 5
Countries citing papers authored by Bin Dai
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
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-authors
The 21 scholars most cited alongside Bin Dai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Compressing Neural Networks using the Variational Information Bottleneck. | 2018 | 28 |
| 2 | 2018 | 17 | |
| 3 | Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models | 2018 | 16 |
| 4 | 2018 | 15 | |
| 5 | 2021 | 15 | |
| 6 | Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders. | 2017 | 2 |
| 7 | 2019 | 2 | |
| 8 | Veiled Attributes of the Variational Autoencoder. | 2017 | 1 |
| 9 | Further Analysis of Outlier Detection with Deep Generative Models | 2020 | 1 |
| 10 | On the Value of Infinite Gradients in Variational Autoencoder Models | 2021 | 1 |
| 11 | 2024 | 1 | |
| 12 | 2021 | 1 | |
| 13 | 2023 | 0 |
About Bin Dai
Bin Dai is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, Ocean Engineering and Mechanical Engineering, having authored 13 papers that have together received 100 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (3 papers), Evacuation and Crowd Dynamics (1 paper), Advanced Chemical Sensor Technologies (1 paper), Natural Language Processing Techniques (1 paper), Image Processing and 3D Reconstruction (1 paper), 3D Shape Modeling and Analysis (1 paper), Advanced machining processes and optimization (1 paper) and Reservoir Engineering and Simulation Methods (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (39 citations), Computational Mathematics (1 citation), Artificial Intelligence (45 citations), Fuel Technology (1 citation) and Ceramics and Composites (5 citations). Bin Dai has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include David Wipf, Gang Hua, Baining Guo, Chen Zhu, John A. D. Aston, Michael L. Myrick, Jinkai Xu, Yu Wang, Guangjun Chen and Huadong Yu. Their work appears in journals such as IEEE Journal of Selected Topics in Signal Processing, Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description, Ceramics International, Journal of Machine Learning Research and Sensors.
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.