This map shows the geographic impact of Daniel Tarlow'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 Tarlow with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Tarlow more than expected).
This network shows the impact of papers produced by Daniel Tarlow. 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 Tarlow. The network helps show where Daniel Tarlow may publish in the future.
Co-authorship network of co-authors of Daniel Tarlow
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Tarlow.
A scholar is included among the top collaborators of Daniel Tarlow 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 Tarlow. Daniel Tarlow is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chen, Zimin, Vincent J. Hellendoorn, Pascal Lamblin, et al.. (2021). PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair. Neural Information Processing Systems. 34.12 indexed citations
3.
Choi, Dami, et al.. (2020). Gradient Estimation with Stochastic Softmax Tricks. Neural Information Processing Systems. 33. 5691–5704.3 indexed citations
4.
Liao, Renjie, Marc Brockschmidt, Daniel Tarlow, et al.. (2018). Graph Partition Neural Networks for Semi-Supervised Classification. arXiv (Cornell University).2 indexed citations
Li, Chengtao, Daniel Tarlow, Alexander L. Gaunt, Marc Brockschmidt, & Nate Kushman. (2016). Neural Program Lattices. International Conference on Learning Representations.7 indexed citations
7.
Gaunt, Alexander L., Marc Brockschmidt, Nate Kushman, & Daniel Tarlow. (2016). Lifelong Perceptual Programming By Example. arXiv (Cornell University).1 indexed citations
8.
Allamanis, Miltiadis, Daniel Tarlow, Andrew D. Gordon, & Wei Yi. (2015). Bimodal Modelling of Source Code and Natural Language. Edinburgh Research Explorer (University of Edinburgh). 2123–2132.87 indexed citations
9.
Maddison, Chris J., Daniel Tarlow, & Tom Minka. (2014). A* Sampling. Neural Information Processing Systems. 27. 3086–3094.57 indexed citations
10.
Eslami, S. M. Ali, Daniel Tarlow, Pushmeet Kohli, & John Winn. (2014). Just-In-Time Learning for Fast and Flexible Inference. Neural Information Processing Systems. 27. 154–162.4 indexed citations
11.
Maddison, Chris J. & Daniel Tarlow. (2014). Structured Generative Models of Natural Source Code. International Conference on Machine Learning. 649–657.19 indexed citations
12.
Meshi, Ofer, et al.. (2014). Learning Structured Models with the AUC Loss and Its Generalizations. International Conference on Artificial Intelligence and Statistics. 841–849.9 indexed citations
13.
Heess, Nicolas, Daniel Tarlow, & John Winn. (2013). Learning to Pass Expectation Propagation Messages. Neural Information Processing Systems. 26. 3219–3227.7 indexed citations
14.
Tarlow, Daniel, Kevin Swersky, Laurent Charlin, Ilya Sutskever, & Rich Zemel. (2013). Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. International Conference on Machine Learning. 199–207.20 indexed citations
15.
Tarlow, Daniel & Richard S. Zemel. (2012). Structured Output Learning with High Order Loss Functions. International Conference on Artificial Intelligence and Statistics. 1212–1220.26 indexed citations
16.
Tarlow, Daniel, Ryan P. Adams, & Richard S. Zemel. (2012). Randomized Optimum Models for Structured Prediction. Digital Access to Scholarship at Harvard (DASH) (Harvard University). 22. 1221–1229.23 indexed citations
17.
Tarlow, Daniel, Inmar E. Givoni, Richard S. Zemel, & Brendan J. Frey. (2011). Graph cuts is a max-product algorithm. Uncertainty in Artificial Intelligence. 671–680.9 indexed citations
18.
Tarlow, Daniel, Dhruv Batra, Pushmeet Kohli, & Vladimir Kolmogorov. (2011). Dynamic Tree Block Coordinate Ascent. International Conference on Machine Learning. 113–120.14 indexed citations
19.
Tarlow, Daniel, Inmar E. Givoni, & Richard S. Zemel. (2010). HOP-MAP: Efficient Message Passing with High Order Potentials. International Conference on Artificial Intelligence and Statistics. 812–819.62 indexed citations
20.
Ross, David A., Daniel Tarlow, & Richard S. Zemel. (2007). Learning Articulated Skeletons from Motion. 2007.1 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.