Citations per year, relative to Dustin Tran Dustin Tran (= 1×)
peers
Lihong Zhi
Countries citing papers authored by Dustin Tran
Since
Specialization
Citations
This map shows the geographic impact of Dustin Tran'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 Dustin Tran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dustin Tran more than expected).
This network shows the impact of papers produced by Dustin Tran. 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 Dustin Tran. The network helps show where Dustin Tran may publish in the future.
Co-authorship network of co-authors of Dustin Tran
This figure shows the co-authorship network connecting the top 25 collaborators of Dustin Tran.
A scholar is included among the top collaborators of Dustin Tran 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 Dustin Tran. Dustin Tran is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lakshminarayanan, Balaji, et al.. (2020). Why Are Bootstrapped Deep Ensembles Not Better.1 indexed citations
7.
Tran, Dustin, Michael W. Dusenberry, Mark van der Wilk, & Danijar Hafner. (2019). Bayesian Layers: A Module for Neural Network Uncertainty. arXiv (Cornell University). 32. 14633–14645.12 indexed citations
8.
Dusenberry, Michael W., et al.. (2019). Measuring Calibration in Deep Learning. Computer Vision and Pattern Recognition. 38–41.15 indexed citations
9.
Tran, Dustin, et al.. (2019). Discrete Flows: Invertible Generative Models of Discrete Data. arXiv (Cornell University). 32. 14692–14701.11 indexed citations
10.
Hafner, Danijar, Dustin Tran, Alex Irpan, Timothy Lillicrap, & James Davidson. (2018). Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors. arXiv (Cornell University).12 indexed citations
11.
Hafner, Danijar, Dustin Tran, Timothy Lillicrap, Alex Irpan, & James Davidson. (2018). Noise Contrastive Priors for Functional Uncertainty. Uncertainty in Artificial Intelligence. 905–914.4 indexed citations
12.
Tran, Dustin, et al.. (2018). Simple, Distributed, and Accelerated Probabilistic Programming. arXiv (Cornell University). 31. 7598–7609.7 indexed citations
13.
Wen, Yeming, Paul Vicol, Jimmy Ba, Dustin Tran, & Roger Grosse. (2018). Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. arXiv (Cornell University).6 indexed citations
14.
Tran, Dustin, Rajesh Ranganath, & David M. Blei. (2017). Deep and Hierarchical Implicit Models.. arXiv (Cornell University).20 indexed citations
15.
Tran, Dustin, Matthew D. Hoffman, Rif A. Saurous, et al.. (2017). Deep Probabilistic Programming. International Conference on Learning Representations.9 indexed citations
16.
Tran, Dustin & David M. Blei. (2017). Comment. Journal of the American Statistical Association. 112(517). 156–158.1 indexed citations
17.
Ranganath, Rajesh, Jaan Altosaar, Dustin Tran, & David M. Blei. (2016). Operator variational inference. Neural Information Processing Systems. 29. 496–504.3 indexed citations
18.
Toulis, Panos, Dustin Tran, & Edoardo M. Airoldi. (2015). Stability and optimality in stochastic gradient descent.. arXiv (Cornell University).2 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.