SMART: An Open Source Data Labeling Platform for Supervised Learning
December 11, 2018 ยท Declared Dead ยท ๐ Journal of machine learning research
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Rob Chew, Michael Wenger, Caroline Kery, Jason Nance, Keith Richards, Emily Hadley, Peter Baumgartner
arXiv ID
1812.06591
Category
stat.ML: Machine Learning (Stat)
Cross-listed
cs.LG
Citations
12
Venue
Journal of machine learning research
Last Checked
4 months ago
Abstract
SMART is an open source web application designed to help data scientists and research teams efficiently build labeled training data sets for supervised machine learning tasks. SMART provides users with an intuitive interface for creating labeled data sets, supports active learning to help reduce the required amount of labeled data, and incorporates inter-rater reliability statistics to provide insight into label quality. SMART is designed to be platform agnostic and easily deployable to meet the needs of as many different research teams as possible. The project website contains links to the code repository and extensive user documentation.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Machine Learning (Stat)
๐ฎ
๐ฎ
The Ethereal
๐ฎ
๐ฎ
The Ethereal
Layer Normalization
๐ฎ
๐ฎ
The Ethereal
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
R.I.P.
๐ป
Ghosted
Variational Inference with Normalizing Flows
๐
๐
The Cartographer
Towards A Rigorous Science of Interpretable Machine Learning
R.I.P.
๐ป
Ghosted
Optimization Methods for Large-Scale Machine Learning
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
๐ป
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
๐ป
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
๐ป
Ghosted