# Weight Agnostic Neural Networks

## TL;DR

This paper explores the potential of neural network architectures to perform tasks without weight training by searching for minimal architectures that can operate with random shared weights, challenging traditional training paradigms.

## Contribution

It introduces a search method for neural architectures that can perform tasks without explicit weight training, emphasizing architecture over learned weights.

## Key findings

- Minimal architectures can perform well without weight training.
- Networks achieve above-chance accuracy on MNIST with random weights.
- The method finds architectures effective across reinforcement and supervised learning.

## 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 https://weightagnostic.github.io/