%0 Journal Article %1 gaier2019weight %A Gaier, Adam %A Ha, David %D 2019 %K deep-learning optimization %T Weight Agnostic Neural Networks %U http://arxiv.org/abs/1906.04358 %X 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/

@article{gaier2019weight, 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/}, added-at = {2019-06-12T17:57:47.000+0200}, author = {Gaier, Adam and Ha, David}, biburl = {https://www.bibsonomy.org/bibtex/275694d5201830304a5971084244b54b8/kirk86}, description = {[1906.04358] Weight Agnostic Neural Networks}, interhash = {0c0433e4cfe0ff86716b648658626593}, intrahash = {75694d5201830304a5971084244b54b8}, keywords = {deep-learning optimization}, note = {cite arxiv:1906.04358}, timestamp = {2019-06-12T17:57:47.000+0200}, title = {Weight Agnostic Neural Networks}, url = {http://arxiv.org/abs/1906.04358}, year = 2019 }