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Growing Neural Network with Shared Parameter

2022-01-17 16:24:17
Ruilin Tong
arXiv_AI
       
Abstract

We propose a general method for growing neural network with shared parameter by matching trained network to new input. By leveraging Hoeffding's inequality, we provide a theoretical base for improving performance by adding subnetwork to existing network. With the theoretical base of adding new subnetwork, we implement a matching method to apply trained subnetwork of existing network to new input. Our method has shown the ability to improve performance with higher parameter efficiency. It can also be applied to trans-task case and realize transfer learning by changing the combination of subnetworks without training on new task.

Abstract (translated)

URL

https://arxiv.org/abs/2201.06500

PDF

https://arxiv.org/pdf/2201.06500.pdf

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