Volltext-Downloads (blau) und Frontdoor-Views (grau)

Weight Agnostic Neural Networks

  • 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/

Export metadata

Additional Services

Search Google Scholar Check availability

Statistics

Show usage statistics
Metadaten
Document Type:Conference Object
Language:English
Author:Adam Gaier, David Ha
Parent Title (German):32nd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, 8-14 December 2019
First Page:5341
Last Page:5355
URL:https://weightagnostic.github.io/
DOI:https://doi.org/10.48550/arXiv.1906.04358
ArXiv Id:http://arxiv.org/abs/1906.04358
Publisher:Curran
Place of publication:Red Hook, NY
Date of first publication:2019/06/11
Departments, institutes and facilities:Fachbereich Informatik
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2019/06/18
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International