Jasper Snoek

14.5k total citations · 2 hit papers
37 papers, 2.2k citations indexed

About

Jasper Snoek is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Jasper Snoek has authored 37 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 14 papers in Computer Vision and Pattern Recognition and 5 papers in Biomedical Engineering. Recurrent topics in Jasper Snoek's work include Video Surveillance and Tracking Methods (6 papers), Human Pose and Action Recognition (6 papers) and Gaussian Processes and Bayesian Inference (6 papers). Jasper Snoek is often cited by papers focused on Video Surveillance and Tracking Methods (6 papers), Human Pose and Action Recognition (6 papers) and Gaussian Processes and Bayesian Inference (6 papers). Jasper Snoek collaborates with scholars based in United States, Canada and Netherlands. Jasper Snoek's co-authors include David R. Kelley, John L. Rinn, Ryan P. Adams, Kevin Swersky, Andrew L. Beam, Benjamin Kompa, Alex Mihailidis, Maxwell L. Bileschi, David Belanger and Cory Y. McLean and has published in prestigious journals such as Genome Research, Analytica Chimica Acta and Marine Ecology Progress Series.

In The Last Decade

Jasper Snoek

36 papers receiving 2.2k citations

Hit Papers

align trajectories

What are hit papers?

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

2016 592 citations David R. Kelley, Jasper Snoek et al. Genome Research profile →
Second opinion needed: communicating uncertainty in medical machine learning

2021 212 citations Benjamin Kompa, Jasper Snoek et al. npj Digital Medicine profile →

Peers

Countries citing papers authored by Jasper Snoek

Since Specialization

Citations

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).

Fields of papers citing papers by Jasper Snoek

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling 2021 ·International Conference on Artificial Intelligence and Statistics ·(unknown), (unknown), Dana H. Brooks, Jennifer Dy, Jasper Snoek	2
2	Second opinion needed: communicating uncertainty in medical machine learning breakdown → 2021 ·npj Digital Medicine ·Benjamin Kompa, Jasper Snoek, Andrew L. Beam	212
3	On the relationship between Normalising Flows and Variational- and Denoising Autoencoders. 2019 ·International Conference on Learning Representations ·Alexey A. Gritsenko, Jasper Snoek, Tim Salimans	0
4	Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift 2019 ·arXiv (Cornell University) ·Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek	92
5	STOCHASTIC GRADIENT LANGEVIN DYNAMICS THAT EXPLOIT NEURAL NETWORK STRUCTURE 2018 ·International Conference on Learning Representations ·Zachary Nado, Jasper Snoek, Roger Grosse, David Duvenaud, (unknown), James Martens	4
6	Winner's Curse? On Pace, Progress, and Empirical Rigor. 2018 ·International Conference on Learning Representations ·D. Sculley, Jasper Snoek, Alexander B. Wiltschko, A R Ostad Rahimi	55
7	Sequential regulatory activity prediction across chromosomes with convolutional neural networks 2018 ·Genome Research ·David R. Kelley, Yakir Reshef, Maxwell L. Bileschi, David Belanger, Cory Y. McLean, Jasper Snoek	264
8	Learning Latent Permutations with Gumbel-Sinkhorn Networks 2018 ·Oxford University Research Archive (ORA) (University of Oxford) ·Gonzalo E. Mena, David Belanger, Scott W. Linderman, Jasper Snoek	19
9	Deep Bayesian Bandits Showdown 2018 ·Carlos Riquelme, George Tucker, Jasper Snoek	9
10	Machine Learning Approaches in Cardiovascular Imaging 2017 ·Circulation Cardiovascular Imaging ·Mir Henglin, (unknown), (unknown), Jasper Snoek, Alexander B. Wiltschko, Susan Cheng	99
11	Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks breakdown → 2016 ·Genome Research ·David R. Kelley, Jasper Snoek, John L. Rinn	592
12	Patient prognosis from vital sign time series: Combining convolutional neural networks with a dynamical systems approach 2015 ·PubMed ·Li-wei H. Lehman, Mohammad M. Ghassemi, Jasper Snoek, Shamim Nemati	5
13	Unobtrusive Detection of Mild Cognitive Impairment in Older Adults Through Home Monitoring 2015 ·IEEE Journal of Biomedical and Health Informatics ·Ahmad Akl, Jasper Snoek, Alex Mihailidis	35
14	Bayesian optimization with unknown constraints 2014 ·Uncertainty in Artificial Intelligence ·Michael A. Gelbart, Jasper Snoek, Ryan P. Adams	26
15	Multi-Task Bayesian Optimization 2013 ·Digital Access to Scholarship at Harvard (DASH) (Harvard University) ·Kevin Swersky, Jasper Snoek, Ryan P. Adams	239
16	Nonparametric guidance of autoencoder representations using label information 2012 ·Journal of Machine Learning Research ·Jasper Snoek, Ryan P. Adams, Hugo Larochelle	33
17	Vision-based posture assessment to detect and categorize compensation during robotic rehabilitation therapy 2012 ·Babak Taati, Rosalie H. Wang, Rajibul Huq, Jasper Snoek, Alex Mihailidis	32
18	Towards a single sensor passive solution for automated fall detection 2011 ·PubMed ·(unknown), Babak Taati, Jasper Snoek, Alex Mihailidis	45
19	Co-limitation by iron and light of Chaetoceros brevis, C. dichaeta and C. calcitrans (Bacillariophyceae) 2001 ·Marine Ecology Progress Series ·Klaas R. Timmermans, (unknown), Bas van der Wagt, Jasper Snoek, RJ Geider, MJW Veldhuis, (unknown), (unknown)	83
20	Automatic identification of algae: neural network analysis of flow cytometric data 1992 ·Journal of Plankton Research ·(unknown), Jasper Snoek, (unknown), (unknown), J.W. Hofstraat, J. Ringelberg	63

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.

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