Google AI Blog: Exploring Weight Agnostic Neural Networks

In “Weight Agnostic Neural Networks” (WANN), we present a first step toward searching specifically for networks with these biases: neural net architectures that can already perform various tasks, even when they use a random shared weight. Our motivation in this work is to question to what extent neural network architectures alone, without learning any weight parameters, can encode solutions for a given task. By exploring such neural network architectures, we present agents that can already perform well in their environment without the need to learn weight parameters. Furthermore, in order to spur progress in this field community, we have also open-sourced the code to reproduce our WANN experiments for the broader research community.

We start with a population of minimal neural network architecture candidates, each with very few connections only, and use a well-established topology search algorithm (NEAT), to evolve the architectures by adding single connections and single nodes one by one.

https://weightagnostic.github.io/

Very interesting results from Google, using evolution-like approach to create network topologies. Thoughts?