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
- Robotic Path Planning Algorithms 1
- Human Pose and Action Recognition 1
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- Artificial Intelligence in Games 1
- Co-authors
- David Wipf (7 shared papers)Chen Zhu (1 shared paper)Baining Guo (1 shared paper)John A. D. Aston (4 shared papers)Gang Hua (3 shared papers)Yu Wang (1 shared paper)Jinkai Xu (1 shared paper)Shen Wang (1 shared paper)
- Journals
- Ceramics International (1 paper)Sensors (1 paper)Journal of Machine Learning Research (1 paper)IEEE Journal of Selected Topics in Signal Processing (1 paper)Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description (1 paper)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Bin Dai
13 papers receiving 109 citations
Peers
Comparison fields: 5 of 45
- Computer Vision and Pattern Recognition 46
- Computational Mathematics 1
- Artificial Intelligence 51
- 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 | 30 |
| 2 | Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models | 2018 | 19 |
| 3 | 2018 | 18 | |
| 4 | 2021 | 17 | |
| 5 | 2018 | 16 | |
| 6 | 2025 | 3 | |
| 7 | Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders. | 2017 | 2 |
| 8 | 2019 | 2 | |
| 9 | Further Analysis of Outlier Detection with Deep Generative Models | 2020 | 1 |
| 10 | Veiled Attributes of the Variational Autoencoder. | 2017 | 1 |
| 11 | On the Value of Infinite Gradients in Variational Autoencoder Models | 2021 | 1 |
| 12 | 2021 | 1 | |
| 13 | 2024 | 1 | |
| 14 | 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 14 papers that have together received 112 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (3 papers), Artificial Intelligence in Games (1 paper), Drilling and Well Engineering (1 paper), Human Motion and Animation (1 paper), Robotic Path Planning Algorithms (1 paper), Human Pose and Action Recognition (1 paper), Hydrocarbon exploration and reservoir analysis (1 paper) and Phase Equilibria and Thermodynamics (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (46 citations), Computational Mathematics (1 citation), Artificial Intelligence (51 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, Chen Zhu, Baining Guo, John A. D. Aston, Gang Hua, Yu Wang, Jinkai Xu, Yu Wang, Shen Wang and Huadong Yu. Their work appears in journals such as Ceramics International, Sensors, Journal of Machine Learning Research, IEEE Journal of Selected Topics in Signal Processing and Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description.
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.