门网控制对ResNets的解释

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Changcun Huang
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引用次数: 0

摘要

本文首先基于lstm的门控制思想构建了ResNet多类别分类的典型解决方案,并由此给出了ResNet体系结构的一般解释,并解释了其性能机制。我们还使用了更多的解来进一步证明这种解释的普遍性。然后将分类结果扩展到具有双层门网络的ResNet类型的通用逼近能力,双层门网络是ResNets的一篇原创论文中提出的一种架构,具有理论和实践意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On an Interpretation of ResNets via Gate-Network Control
This letter first constructs a typical solution of ResNets for multicategory classifications based on the idea of the gate control of LSTMs, from which a general interpretation of the ResNet architecture is given and the performance mechanism is explained. We also use more solutions to further demonstrate the generality of that interpretation. The classification result is then extended to the universal-approximation capability of the type of ResNet with two-layer gate networks, an architecture that was proposed in an original paper of ResNets and has both theoretical and practical significance.
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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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