Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks
2016592 citationsDavid R. Kelley, Jasper Snoek et al.Genome Researchprofile →
Second opinion needed: communicating uncertainty in medical machine learning
2021212 citationsBenjamin Kompa, Jasper Snoek et al.npj Digital Medicineprofile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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This map shows the geographic impact of Jasper Snoek'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 Jasper Snoek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jasper Snoek more than expected).
This network shows the impact of papers produced by Jasper Snoek. 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 Jasper Snoek. The network helps show where Jasper Snoek may publish in the future.
Co-authorship network of co-authors of Jasper Snoek
This figure shows the co-authorship network connecting the top 25 collaborators of Jasper Snoek.
A scholar is included among the top collaborators of Jasper Snoek 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 Jasper Snoek. Jasper Snoek is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Brooks, Dana H., et al.. (2021). Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling. International Conference on Artificial Intelligence and Statistics. 3961–3969.2 indexed citations
2.
Kompa, Benjamin, Jasper Snoek, & Andrew L. Beam. (2021). Second opinion needed: communicating uncertainty in medical machine learning. npj Digital Medicine. 4(1). 4–4.212 indexed citations breakdown →
3.
Gritsenko, Alexey A., Jasper Snoek, & Tim Salimans. (2019). On the relationship between Normalising Flows and Variational- and Denoising Autoencoders.. International Conference on Learning Representations.
4.
Ovadia, Yaniv, Emily Fertig, Jie Ren, et al.. (2019). Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. arXiv (Cornell University). 32. 13969–13980.92 indexed citations
5.
Sculley, D., Jasper Snoek, Alexander B. Wiltschko, & A R Ostad Rahimi. (2018). Winner's Curse? On Pace, Progress, and Empirical Rigor.. International Conference on Learning Representations.55 indexed citations
6.
Nado, Zachary, et al.. (2018). STOCHASTIC GRADIENT LANGEVIN DYNAMICS THAT EXPLOIT NEURAL NETWORK STRUCTURE. International Conference on Learning Representations.4 indexed citations
7.
Mena, Gonzalo E., David Belanger, Scott W. Linderman, & Jasper Snoek. (2018). Learning Latent Permutations with Gumbel-Sinkhorn Networks. Oxford University Research Archive (ORA) (University of Oxford).19 indexed citations
Kelley, David R., Jasper Snoek, & John L. Rinn. (2016). Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Research. 26(7). 990–999.592 indexed citations breakdown →
Gelbart, Michael A., Jasper Snoek, & Ryan P. Adams. (2014). Bayesian optimization with unknown constraints. Uncertainty in Artificial Intelligence. 250–259.26 indexed citations
15.
Swersky, Kevin, Jasper Snoek, & Ryan P. Adams. (2013). Multi-Task Bayesian Optimization. Digital Access to Scholarship at Harvard (DASH) (Harvard University). 26. 2004–2012.239 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.