The Building Blocks of Interpretability
Interpretability techniques are normally studied in isolation. We explore the powerful interfaces that arise when you combine them — and the rich structure of this combinatorial space.
Using Artificial Intelligence to Augment Human Intelligence
By creating user interfaces which let us work with the representations inside machine learning models, we can give people new tools for reasoning.
Sequence Modeling with CTC
A visual guide to Connectionist Temporal Classification, an algorithm used to train deep neural networks in speech recognition, handwriting recognition and other sequence problems.
Feature Visualization
How neural networks build up their understanding of images.
Why Momentum Really Works
We often think of optimization with momentum as a ball rolling down a hill. This isn’t wrong, but there is much more to the story.
Research Debt
Science is a human activity. When we fail to distill and explain research, we accumulate a kind of debt...
Experiments in Handwriting with a Neural Network
Several interactive visualizations of a generative model of handwriting. Some are fun, some are serious.
Deconvolution and Checkerboard Artifacts
When we look very closely at images generated by neural networks, we often see a strange checkerboard pattern of artifacts.
How to Use t-SNE Effectively
Although extremely useful for visualizing high-dimensional data, t-SNE plots can sometimes be mysterious or misleading.
Attention and Augmented Recurrent Neural Networks
A visual overview of neural attention, and the powerful extensions of neural networks being built on top of it.