Josh Andres

TL;DR: A first user evaluation by 10 data scientists of an experimental system, AutoAIViz, that aims to visualize AutoAI's model generation process finds that the proposed system helps users to complete the data science tasks, and increases their understanding, toward the goal of increasing trust in the AutoAI system.

TL;DR: The authors provide a grounded account of the work of labeling teams with domain experts, including the experiences of labeling, collaborative configurations and work-practices, and quality issues, and show three major patterns in the social design of ground truth data: principled design, iterative design and improvisational design.

TL;DR: In this paper, an automated machine learning (AutoML) system that aims to leverage the latest ML automation techniques to support data science projects is introduced. But the system is limited to a single dataset and the user does not have access to the entire dataset.

TL;DR: In this article, an automated machine learning (AutoML) system that aims to leverage the latest ML automation techniques to support data science projects is presented. But, the system only needs data workers to upload their dataset, then the system can automatically suggest ML configurations, preprocess data, select algorithm, and train the model.

TL;DR: This work presents "Ena", a novel EEG-eBike system that draws from the user's neural activity to determine when the user is in a state of peripheral awareness to regulate engine support, and suggests that Ena suggests that the work facilitates a safe and enjoyable human-computer integration experience.