Countries citing papers authored by Daniel Ritchie
Since
Specialization
Citations
This map shows the geographic impact of Daniel Ritchie'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 Daniel Ritchie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Ritchie more than expected).
This network shows the impact of papers produced by Daniel Ritchie. 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 Daniel Ritchie. The network helps show where Daniel Ritchie may publish in the future.
Co-authorship network of co-authors of Daniel Ritchie
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Ritchie.
A scholar is included among the top collaborators of Daniel Ritchie 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 Daniel Ritchie. Daniel Ritchie is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chaudhuri, Siddhartha, Daniel Ritchie, Jiajun Wu, Kai Xu, & Hao Zhang. (2020). Learning Generative Models of 3D Structures. Computer Graphics Forum. 39(2). 643–666.46 indexed citations
9.
Wang, Kai, et al.. (2019). PlanIT. ACM Transactions on Graphics. 38(4). 1–15.112 indexed citations
10.
Liu, Yunchao, Zheng Wu, Daniel Ritchie, et al.. (2018). Learning to Describe Scenes with Programs. International Conference on Learning Representations.9 indexed citations
11.
Ellis, Kevin, Daniel Ritchie, Armando Solar-Lezama, & Joshua B. Tenenbaum. (2018). Learning to Infer Graphics Programs from Hand-Drawn Images. DSpace@MIT (Massachusetts Institute of Technology). 31. 6059–6068.33 indexed citations
Ritchie, Daniel, Anna Thomas, Pat Hanrahan, & Noah D. Goodman. (2016). Neurally-Guided Procedural Models: Learning to Guide Procedural Models with Deep Neural Networks.. arXiv (Cornell University).3 indexed citations
14.
Ritchie, Daniel, Anna Thomas, Pat Hanrahan, & Noah D. Goodman. (2016). Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks. Neural Information Processing Systems. 29. 622–630.4 indexed citations
15.
Ritchie, Daniel, Andreas Stuhlmüller, & Noah D. Goodman. (2016). C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching. International Conference on Artificial Intelligence and Statistics. 28–37.8 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.