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).
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
12 of 12 papers shown
1.
Shin, Richard, Sam Thomson, Charles Chen, et al.. (2021). Constrained Language Models Yield Few-Shot Semantic Parsers. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 7699–7715.87 indexed citations
2.
Fox, Roy, Richard Shin, Sanjay Krishnan, et al.. (2018). Parametrized Hierarchical Procedures for Neural Programming. International Conference on Learning Representations.7 indexed citations
3.
Shin, Richard, et al.. (2018). Differentiable Neural Network Architecture Search. International Conference on Learning Representations.38 indexed citations
4.
Shin, Richard, Illia Polosukhin, & Dawn Song. (2018). Towards Specification-Directed Program Repair. International Conference on Learning Representations.2 indexed citations
5.
Shin, Richard, Marc Brockschmidt, Miltiadis Allamanis, & Oleksandr Polozov. (2018). Program Synthesis with Learned Code Idioms.1 indexed citations
6.
Fox, Roy, Richard Shin, William Paul, et al.. (2018). Hierarchical Imitation Learning via Variational Inference of Control Programs.1 indexed citations
7.
Shin, Richard, et al.. (2017). Making Neural Programming Architectures Generalize via Recursion. arXiv (Cornell University).6 indexed citations
Liu, Chang, Xin Wang, Richard Shin, Joseph E. Gonzalez, & Dawn Song. (2017). Neural Code Completion.28 indexed citations
10.
Shin, Richard, Alexander A. Alemi, Geoffrey Irving, & Oriol Vinyals. (2017). Tree-Structured Variational Autoencoder.1 indexed citations
11.
Weinberger, Joel, Prateek Saxena, Devdatta Akhawe, Matthew Finifter, & Richard Shin. (2011). An Empirical Analysis of XSS Sanitization in Web Application Frameworks. UC Berkeley.14 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.