通过网关网络控制对ResNets的解释
1Shuitu Institute of Applied Mathematics, Chongqing 400700, P.R.C. cchuang@mail.ustc.edu.cn.
Neural computation
|July 12, 2023
概括
本研究提出了用于多类分类的剩余网络 (ResNets) 的新解读,与长短期内存 (LSTM) 门控制相平行. 该框架解释了ResNet的性能,并展示了它们的通用近似能力.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 剩余网络 (ResNets) 被广泛用于深度学习任务.
- 了解ResNet架构的基本机制对于进一步的进步至关重要.
- 多类别分类仍然是机器学习中的一个具有挑战性的问题.
研究的目的:
- 使用ResNets.构建一个典型的解决方案,用于多类别的分类.
- 提供ResNet架构及其性能机制的一般解释.
- 探索特定ResNet架构的通用近似能力.
主要方法:
- 构建一个ResNet解决方案,灵感来自长短期内存 (LSTM) 网络的门控制机制.
- 将这种解释应用于多类分类任务.
- 分析使用双层网关网络的ResNets的性能.
主要成果:
- 建立了ResNet架构及其性能机制的一般解释.
- 这种解释的普遍性通过多个解决方案来证明.
- 展示了ResNets与双层网关网络的通用近似能力.
结论:
- 拟议的解释为ResNet功能提供了新的视角.
- ResNets,特别是具有双层门网络的ResNets,对于分类具有重要的理论和实践意义.
- 这项工作有助于更深入地了解深度学习架构.
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