Rui Shu
- Developmental Neuroscience top 10%
- Cognitive Neuroscience top 10%
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- Generative Adversarial Networks and Image Synthesis 3
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- Explainable Artificial Intelligence (XAI) 2
- Adversarial Robustness in Machine Learning 2
- Domain Adaptation and Few-Shot Learning 2
- Gaussian Processes and Bayesian Inference 2
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- Cyclone Separators and Fluid Dynamics 1
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- Wireless Networks and Protocols 1
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- Protein Structure and Dynamics 1
- Co-authors
- Aleksandr ShcheglovitovVittorio SebastianoJoachim HallmayerThomas PortmannAnna KrawiszJonathan A. BernsteinRicardo E. DolmetschStefano Ermon
- Journals
- Nature (1 paper)Clean Technologies and Environmental Policy (1 paper)PuSH - Publication Server of Helmholtz Zentrum München (1 paper)
- Partner nations
- United StatesChinaSwitzerland
In The Last Decade
Rui Shu
10 papers receiving 466 citations
Peers
Comparison fields: 5 of 76
- Developmental Neuroscience 36
- Cognitive Neuroscience 132
- Genetics 175
- Aging 10
- Cellular and Molecular Neuroscience 85
Countries citing papers authored by Rui Shu
This map shows the geographic impact of Rui Shu'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 Rui Shu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rui Shu more than expected).
Fields of papers citing papers by Rui Shu
This network shows the impact of papers produced by Rui Shu. 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 Rui Shu. The network helps show where Rui Shu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Rui Shu, 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 | 2023 | 6 | |
| 2 | 2021 | 2 | |
| 3 | Weakly Supervised Disentanglement with Guarantees | 2020 | 7 |
| 4 | Domain Adaptation for Human Fall Detection Using WiFi Channel State Information. | 2020 | 1 |
| 5 | Rethinking Style and Content Disentanglement in Variational Autoencoders | 2018 | 3 |
| 6 | Constructing Unrestricted Adversarial Examples with Generative Models | 2018 | 26 |
| 7 | Bayesian optimization and attribute adjustment | 2018 | 2 |
| 8 | Amortized Inference Regularization | 2018 | 8 |
| 9 | A DIRT-T Approach to Unsupervised Domain Adaptation | 2018 | 84 |
| 10 | 2013 | 341 |
About Rui Shu
Rui Shu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Theory and Mathematics, having authored 10 papers that have together received 480 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (3 papers), Explainable Artificial Intelligence (XAI) (2 papers), Adversarial Robustness in Machine Learning (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Gaussian Processes and Bayesian Inference (2 papers), Cyclone Separators and Fluid Dynamics (1 paper), Wireless Networks and Protocols (1 paper) and Protein Structure and Dynamics (1 paper). The work is most often cited by research in Developmental Neuroscience (36 citations), Cognitive Neuroscience (132 citations) and Genetics (175 citations). Rui Shu has collaborated with scholars based in United States, China and Switzerland. Frequent co-authors include Aleksandr Shcheglovitov, Vittorio Sebastiano, Joachim Hallmayer, Thomas Portmann, Anna Krawisz, Jonathan A. Bernstein, Ricardo E. Dolmetsch, Stefano Ermon, Masayuki Yazawa and Wendy Froehlich. Their work appears in journals such as Nature, Clean Technologies and Environmental Policy and PuSH - Publication Server of Helmholtz Zentrum München.
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.