_:nc6b2327747d6445dbab6957c30bd0bb5xb0
<http://schema.org/description>
"To mitigate the pain of manually tuning hyperparameters of deep neural\nnetworks, automated machine learning (AutoML) methods have been developed to\nsearch for an optimal set of hyperparameters in large combinatorial search\nspaces. However, the search results of AutoML methods significantly depend on\ninitial configurations, making it a non-trivial task to find a proper\nconfiguration. Therefore, human intervention via a visual analytic approach\nbears huge potential in this task. In response, we propose HyperTendril, a\nweb-based visual analytics system that supports user-driven hyperparameter\ntuning processes in a model-agnostic environment. HyperTendril takes a novel\napproach to effectively steering hyperparameter optimization through an\niterative, interactive tuning procedure that allows users to refine the search\nspaces and the configuration of the AutoML method based on their own insights\nfrom given results. Using HyperTendril, users can obtain insights into the\ncomplex behaviors of various hyperparameter search algorithms and diagnose\ntheir configurations. In addition, HyperTendril supports variable importance\nanalysis to help the users refine their search spaces based on the analysis of\nrelative importance of different hyperparameters and their interaction effects.\nWe present the evaluation demonstrating how HyperTendril helps users steer\ntheir tuning processes via a longitudinal user study based on the analysis of\ninteraction logs and in-depth interviews while we deploy our system in a\nprofessional industrial environment."
<https://deepai.org/publication/hypertendril-visual-analytics-for-user-driven-hyperparameter-optimization-of-deep-neural-networks>