Hyperband: a novel bandit-based approach to hyperparameter optimization
- Computational Theory and Mathematics
- Artificial Intelligence
- Management Science and Operations Research
In The Last Decade
doi.org/w12292441 →Countries where authors are citing Hyperband: a novel bandit-based approach to hyperparameter optimization
This map shows the geographic impact of Hyperband: a novel bandit-based approach to hyperparameter optimization. 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 Hyperband: a novel bandit-based approach to hyperparameter optimization with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hyperband: a novel bandit-based approach to hyperparameter optimization more than expected).
Fields of papers citing Hyperband: a novel bandit-based approach to hyperparameter optimization
This network shows the impact of Hyperband: a novel bandit-based approach to hyperparameter optimization. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Hyperband: a novel bandit-based approach to hyperparameter optimization.
About Hyperband: a novel bandit-based approach to hyperparameter optimization
This paper, published in 2017, received 510 indexed citations . Written by Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh and Ameet Talwalkar covering the research area of Computational Theory and Mathematics, Artificial Intelligence and Management Science and Operations Research. It is primarily cited by scholars working on Artificial Intelligence (238 citations), Computer Vision and Pattern Recognition (92 citations) and Electrical and Electronic Engineering (53 citations). Published in Journal of Machine Learning Research.
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This paper is also available at doi.org/w12292441.