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Main Authors: Wang, Tangjun, Bao, Chenglong, Shi, Zuoqiang
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2403.15726
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author Wang, Tangjun
Bao, Chenglong
Shi, Zuoqiang
author_facet Wang, Tangjun
Bao, Chenglong
Shi, Zuoqiang
contents In this paper, we study the partial differential equation models of neural networks. Neural network can be viewed as a map from a simple base model to a complicate function. Based on solid analysis, we show that this map can be formulated by a convection-diffusion equation. This theoretically certified framework gives mathematical foundation and more understanding of neural networks. Moreover, based on the convection-diffusion equation model, we design a novel network structure, which incorporates diffusion mechanism into network architecture. Extensive experiments on both benchmark datasets and real-world applications validate the performance of the proposed model.
format Preprint
id arxiv_https___arxiv_org_abs_2403_15726
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Convection-Diffusion Equation: A Theoretically Certified Framework for Neural Networks
Wang, Tangjun
Bao, Chenglong
Shi, Zuoqiang
Machine Learning
In this paper, we study the partial differential equation models of neural networks. Neural network can be viewed as a map from a simple base model to a complicate function. Based on solid analysis, we show that this map can be formulated by a convection-diffusion equation. This theoretically certified framework gives mathematical foundation and more understanding of neural networks. Moreover, based on the convection-diffusion equation model, we design a novel network structure, which incorporates diffusion mechanism into network architecture. Extensive experiments on both benchmark datasets and real-world applications validate the performance of the proposed model.
title Convection-Diffusion Equation: A Theoretically Certified Framework for Neural Networks
topic Machine Learning
url https://arxiv.org/abs/2403.15726