Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/63857

Easy and Efficient Hyperparameter Optimization to Address Some Artificial Intelligence “ilities”

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Title:Easy and Efficient Hyperparameter Optimization to Address Some Artificial Intelligence “ilities”
Authors:Bihl, Trevor
Schoenbeck, Joe
Steeneck, Daniel
Jordan, Jeremy
Keywords:Big Data and Analytics: Pathways to Maturity
hyperparameters
machine learning
professional practice
repeatability
Date Issued:07 Jan 2020
Abstract:Artificial Intelligence (AI), has many benefits, including the ability to find complex patterns, automation, and meaning making. Through these benefits, AI has revolutionized image processing among numerous other disciplines. AI further has the potential to revolutionize other domains; however, this will not happen until we can address the “ilities”: repeatability, explain-ability, reliability, use-ability, trust-ability, etc. Notably, many problems with the “ilities” are due to the artistic nature of AI algorithm development, especially hyperparameter determination. AI algorithms are often crafted products with the hyperparameters learned experientially. As such, when applying the same algorithm to new problems, the algorithm may not perform due to inappropriate settings. This research aims to provide a straightforward and reliable approach to automatically determining suitable hyperparameter settings when given an AI algorithm. Results, show reasonable performance is possible and end-to-end examples are given for three deep learning algorithms and three different data problems.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63857
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.118
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Big Data and Analytics: Pathways to Maturity


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