相关实验视频
Updated: Jun 28, 2025

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
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在浮点运算下,ReLU和阶段网络的表达力
Yeachan Park1, Geonho Hwang1, Wonyeol Lee2
1Korea Institute for Advanced Study, Seoul, 02455, Republic of Korea.
概括
神经网络甚至可以通过浮点算法来记住和近似函数,匹配理论极限. 这项研究弥合了理论神经网络能力和实际计算机实现之间的差距.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习理论机器学习理论
背景情况:
- 关于神经网络表达力的现有研究通常假定理想的实值计算.
- 实际的神经网络执行使用有限精度浮点数和不准确的操作.
- 这种差异限制了理论研究结果对现实场景的适用性.
研究的目的:
- 用现实的浮点算法分析神经网络的表达力.
- 在实际计算约束下研究记忆和普遍近似能力.
- 弥合理论模型和实际神经网络实现之间的差距.
主要方法:
- 通过浮点运算 (有限显数,无限指数) 分析神经网络表达力.
- 将分析扩展到具有有限显数和指数的浮点运算 (例如,IEEE 754).
- 构建神经网络,展示记忆和通用近似特性.
主要成果:
- 具有二进制值或ReLU单元的神经网络可以使用浮点算法来记住有限的输入/输出对.
- 这些网络可以在浮点条件下在任意误差范围内近似计算任何连续函数.
- 需要的参数数量与通过精确的数学运算获得的经典结果相匹配.
结论:
- 神经网络保留了显著的表达能力 (记忆,普遍近似),即使在实际的浮点计算.
- 关于神经网络能力的理论发现比以前假设的更适用于现实世界的系统.
- 结果适用于各种浮点格式,包括IEEE 754和bfloat16.
相关概念视频
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