HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks

no code implementations • 4 Sep 2020 • Heungseok Park, Yoonsoo Nam, Ji-Hoon Kim, Jaegul Choo

HyperTendril takes a novel approach to effectively steering hyperparameter optimization through an iterative, interactive tuning procedure that allows users to refine the search spaces and the configuration of the AutoML method based on their own insights from given results.

Hyperparameter Optimization

CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms

no code implementations • 8 Oct 2018 • Jinwoong Kim, Minkyu Kim, Heungseok Park, Ernar Kusdavletov, Dongjun Lee, Adrian Kim, Ji-Hoon Kim, Jung-Woo Ha, Nako Sung

Many hyperparameter optimization (HyperOpt) methods assume restricted computing resources and mainly focus on enhancing performance.

BIG-bench Machine Learning Hyperparameter Optimization +1

NSML: Meet the MLaaS platform with a real-world case study

no code implementations • 8 Oct 2018 • Hanjoo Kim, Minkyu Kim, Dongjoo Seo, Jinwoong Kim, Heungseok Park, Soeun Park, Hyunwoo Jo, KyungHyun Kim, Youngil Yang, Youngkwan Kim, Nako Sung, Jung-Woo Ha

The boom of deep learning induced many industries and academies to introduce machine learning based approaches into their concern, competitively.

BIG-bench Machine Learning Management