Machine learning models often learn latent embedding representations tha...
Machine learning (ML) models can fail in unexpected ways in the real wor...
Existing novice-friendly machine learning (ML) modeling tools center aro...
Lack of diversity in data collection has caused significant failures in
...
Interfaces for machine learning (ML), information and visualizations abo...
The confusion matrix, a ubiquitous visualization for helping people eval...
Existing research on making sense of deep neural networks often focuses ...
We aim to increase the flexibility at which a data worker can choose the...
Deep neural networks (DNNs) are now commonly used in many domains. Howev...
Deep learning's great success motivates many practitioners and students ...
Deep neural networks (DNNs) are increasingly powering high-stakes
applic...
The success of deep learning solving previously-thought hard problems ha...
In recent years, machine learning (ML) has gained significant popularity...
As deep neural networks are increasingly used in solving high-stake prob...
The growing capability and accessibility of machine learning has led to ...
Deep learning is increasingly used in decision-making tasks. However,
un...
We are developing an interactive graph exploration system called Graph
P...
We present an interactive system enabling users to manipulate images to
...
The rapidly growing body of research in adversarial machine learning has...
Deep learning has recently seen rapid development and significant attent...
Knowing where people live is a fundamental component of many decision ma...
Deep neural networks (DNNs) have achieved great success in solving a var...