Distill — Latest articles about machine learning
Understanding Convolutions on Graphs Ameya Daigavane, Balaraman Ravindran, and Gaurav Aggarwal Understanding the building blocks and design choices of graph neural networks. A Gentle Introduction to Graph Neural Networks Benjamin Sanchez-Lengeling, Emily Reif, Adam Pearce, and Alexander B. Wiltschko What components are needed for building learning algorithms that leverage the structure and propert
Feature Visualization
If we want to understand individual features, we can search for examples where they have high values — either for a neuron at an individual position, or for an entire channel. We used the channel objective to create most of the images in this article. If we want to understand a layer as a whole, we can use the DeepDream objective , searching for images the layer finds “interesting.” And if we want
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. With the growing success of neural networks, there is a corresponding need to be able to explain their decisions — including building confidence about how they will behave in the real-world, detecting model bias, an
Using Artificial Intelligence to Augment Human Intelligence
Sentence from Pride and Prejudice by Jane Austen. Interpolated by the authors. Inspired by experiments done by the novelist Robin Sloan Such generative interfaces provide a kind of cartography of generative models, ways for humans to explore and make meaning using those models. We saw earlier that the font model automatically infers relatively deep principles about font design, and makes them avai
Experiments in Handwriting with a Neural Network
Neural networks are an extremely successful approach to machine learning, but it’s tricky to understand why they behave the way they do. This has sparked a lot of interest and effort around trying to understand and visualize them, which we think is so far just scratching the surface of what is possible. In this article we will try to push forward in this direction by taking a generative model of h
Deconvolution and Checkerboard Artifacts
When we look very closely at images generated by neural networks, we often see a strange checkerboard pattern of artifacts. It’s more obvious in some cases than others, but a large fraction of recent models exhibit this behavior. Mysteriously, the checkerboard pattern tends to be most prominent in images with strong colors. What’s going on? Do neural networks hate bright colors? The actual cause o
Attention and Augmented Recurrent Neural Networks
Recurrent neural networks are one of the staples of deep learning, allowing neural networks to work with sequences of data like text, audio and video. They can be used to boil a sequence down into a high-level understanding, to annotate sequences, and even to generate new sequences from scratch! x0 y0 x1 y1 x2 y2 x3 y3 One cell... can be used over... and over... and over... x4 y4 again. The basic
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