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

09/04/2020
by   Heungseok Park, et al.
0

To mitigate the pain of manually tuning hyperparameters of deep neural networks, automated machine learning (AutoML) methods have been developed to search for an optimal set of hyperparameters in large combinatorial search spaces. However, the search results of AutoML methods significantly depend on initial configurations, making it a non-trivial task to find a proper configuration. Therefore, human intervention via a visual analytic approach bears huge potential in this task. In response, we propose HyperTendril, a web-based visual analytics system that supports user-driven hyperparameter tuning processes in a model-agnostic environment. 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. Using HyperTendril, users can obtain insights into the complex behaviors of various hyperparameter search algorithms and diagnose their configurations. In addition, HyperTendril supports variable importance analysis to help the users refine their search spaces based on the analysis of relative importance of different hyperparameters and their interaction effects. We present the evaluation demonstrating how HyperTendril helps users steer their tuning processes via a longitudinal user study based on the analysis of interaction logs and in-depth interviews while we deploy our system in a professional industrial environment.

READ FULL TEXT

page 1

page 6

research
08/30/2021

To tune or not to tune? An Approach for Recommending Important Hyperparameters

Novel technologies in automated machine learning ease the complexity of ...
research
02/13/2019

ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

To relieve the pain of manually selecting machine learning algorithms an...
research
12/02/2018

Automatic hyperparameter selection in Autodock

Autodock is a widely used molecular modeling tool which predicts how sma...
research
12/02/2020

VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization

During the training phase of machine learning (ML) models, it is usually...
research
10/08/2018

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

Many hyperparameter optimization (HyperOpt) methods assume restricted co...
research
07/13/2022

Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning

Tackling new machine learning problems with neural networks always means...
research
11/08/2021

Explaining Hyperparameter Optimization via Partial Dependence Plots

Automated hyperparameter optimization (HPO) can support practitioners to...

Please sign up or login with your details

Forgot password? Click here to reset