Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Researchers and developers often need to explore the properties of a specific embedding, and one way to analyze embeddings is to visualize them. We present the Embedding Projector, a tool for interactive visualization and interpretation of embeddings.
@article{arxiv.1611.05469,
title = {Embedding Projector: Interactive Visualization and Interpretation of Embeddings},
author = {Daniel Smilkov and Nikhil Thorat and Charles Nicholson and Emily Reif and Fernanda B. Viégas and Martin Wattenberg},
journal= {arXiv preprint arXiv:1611.05469},
year = {2016}
}
Comments
Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems