Weight Agnostic Neural Networks
Published in
Neural Information Processing Systems(2019)External Links:
TL
TL;DR
This work proposes a search method for neural network architectures that can already perform a task without any explicit weight training, and demonstrates that this method can find minimal neural network architecture that can perform several reinforcement learning tasks without weight training.
Abstract
Not all neural network architectures are created equal, some perform much better than others for certain tasks. But how important are the weight parameters of a neural network compared to its architecture? In this work, we question to what extent neural network architectures alone, without learning any weight parameters, can encode solutions for a given task. We propose a search method for neural network architectures that can already perform a task without any explicit weight training. To evaluate these networks, we populate the connections with a single shared weight parameter sampled from a uniform random distribution, and measure the expected performance. We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training. On a supervised learning domain, we find network architectures that achieve much higher than chance accuracy on MNIST using random weights. Interactive version of this paper at this https URL
References132 items
1
A critique of pure learning and what artificial neural networks can learn from animal brains
2
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
3
Evaluating the Search Phase of Neural Architecture Search
4
Random Search and Reproducibility for Neural Architecture Search
5