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.
Countries citing papers authored by James Bergstra
Since
Specialization
Citations
This map shows the geographic impact of James Bergstra'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 James Bergstra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Bergstra more than expected).
This network shows the impact of papers produced by James Bergstra. 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 James Bergstra. The network helps show where James Bergstra may publish in the future.
Co-authorship network of co-authors of James Bergstra
This figure shows the co-authorship network connecting the top 25 collaborators of James Bergstra.
A scholar is included among the top collaborators of James Bergstra 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 James Bergstra. James Bergstra is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bergstra, James, Brent Komer, Chris Eliasmith, Dan Yamins, & David Cox. (2015). Hyperopt: a Python library for model selection and hyperparameter optimization. 8(1). 14008–14008.636 indexed citations breakdown →
Hunsberger, Eric, Peter Blouw, James Bergstra, & Chris Eliasmith. (2013). A Neural Model of Human Image Categorization. Cognitive Science. 35(35).2 indexed citations
Bergstra, James, Dan Yamins, & David Cox. (2013). Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. Proceedings of the Python in Science Conferences. 13–19.485 indexed citations breakdown →
Courville, Aaron, James Bergstra, & Yoshua Bengio. (2011). A Spike and Slab Restricted Boltzmann Machine. International Conference on Artificial Intelligence and Statistics. 233–241.41 indexed citations
13.
Bengio, Yoshua, Aaron Courville, & James Bergstra. (2011). Unsupervised Models of Images by Spike-and-Slab RBMs. International Conference on Machine Learning. 1145–1152.38 indexed citations
14.
Mesnil, Grégoire, Yann Dauphin, Xavier Glorot, et al.. (2011). Unsupervised and Transfer Learning Challenge: a Deep Learning Approach. International Conference on Machine Learning. 97–110.90 indexed citations
15.
Bergstra, James, Olivier Breuleux, Frédéric Bastien, et al.. (2010). Theano: A CPU and GPU Math Compiler in Python. Proceedings of the Python in Science Conferences. 18–24.686 indexed citations breakdown →
Larochelle, Hugo, Dumitru Erhan, Aaron Courville, James Bergstra, & Yoshua Bengio. (2007). An empirical evaluation of deep architectures on problems with many factors of variation. 473–480.665 indexed citations breakdown →
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.