Richard Shin
- Artificial Intelligence top 10%
- Information Systems top 10%
- Computer Vision and Pattern Recognition top 10%
- Software top 10%
- Signal Processing
- Co-authors
- Dawn SongEmmanouil Antonios PlataniosBenjamin Van DurmeSubhro RoyDan KleinSam ThomsonCharles ChenAdam Pauls
- Topics
- Natural Language Processing Techniques (3 papers)Machine Learning and Algorithms (2 papers)Topic Modeling (2 papers)
- Journals
- Electromagnetic wavesarXiv (Cornell University)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Partner nations
- United States
In The Last Decade
Richard Shin
12 papers receiving 191 citations
Peers
Comparison fields: 5 of 42
- Artificial Intelligence 143
- Information Systems 68
- Computer Vision and Pattern Recognition 66
- Software 23
- Signal Processing 21
Countries citing papers authored by Richard Shin
This map shows the geographic impact of Richard Shin'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 Richard Shin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Shin more than expected).
Fields of papers citing papers by Richard Shin
This network shows the impact of papers produced by Richard Shin. 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 Richard Shin. The network helps show where Richard Shin may publish in the future.
Co-authorship network of co-authors of Richard Shin
This figure shows the co-authorship network connecting the top 25 collaborators of Richard Shin. A scholar is included among the top collaborators of Richard Shin 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 Richard Shin. Richard Shin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 87 | |
| 2 | Parametrized Hierarchical Procedures for Neural Programming | 7 |
| 3 | Differentiable Neural Network Architecture Search | 38 |
| 4 | Towards Specification-Directed Program Repair | 2 |
| 5 | Program Synthesis with Learned Code Idioms | 1 |
| 6 | Hierarchical Imitation Learning via Variational Inference of Control Programs | 1 |
| 7 | Making Neural Programming Architectures Generalize via Recursion | 6 |
| 8 | 8 | |
| 9 | Tree-Structured Variational Autoencoder | 1 |
| 10 | Neural Code Completion | 28 |
| 11 | An Empirical Analysis of XSS Sanitization in Web Application Frameworks | 14 |
| 12 | 6 |
About Richard Shin
Richard Shin is a scholar working on Software, Artificial Intelligence and Signal Processing, having authored 12 papers that have together received 199 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (3 papers), Machine Learning and Algorithms (2 papers) and Topic Modeling (2 papers). The work is most often cited by research in Software (23 citations), Artificial Intelligence (143 citations) and Computer Vision and Pattern Recognition (66 citations). Richard Shin has collaborated with scholars based in United States. Frequent co-authors include Dawn Song, Emmanouil Antonios Platanios, Benjamin Van Durme, Subhro Roy, Dan Klein, Sam Thomson, Charles Chen, Adam Pauls, Jason Eisner and Xin Wang. Their work appears in journals such as Electromagnetic waves, arXiv (Cornell University) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.