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推进神经网络校准:梯度衰减在Softmax优化大边际优化中的作用
1School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu, China; Internet of Things Technology Application Engineering Research Center, Ministry of Education, Wuxi, Jiangsu, China.
概括
在Softmax中的新型超参数调节梯度衰变,改善模型概括和校准. 较大的衰变率有效地解决了过度自信,超过了校准后方法.
科学领域:
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
- 神经网络优化神经网络优化
背景情况:
- 现代神经网络经常表现出过度自信和校准问题.
- 大幅利软马克斯方法旨在提高歧视力.
- 了解梯度动力学对于模型性能至关重要.
研究的目的:
- 引入一个新的超参数来控制Softmax中的概率依赖梯度衰变.
- 调查梯度衰减率对模型概括和校准的影响.
- 探索梯度衰变,课程学习和利普希茨约束之间的关系.
主要方法:
- 一个新的Softmax超参数的理论和经验分析.
- 检查梯度衰变行为与不同的样本概率 (凸/凸).
- 建议和评估一种新的动态梯度衰变升温策略.
主要成果:
- 较小的梯度衰变诱导了课程学习,但加剧了过度自信.
- 较大的梯度衰减显著改善了模型校准,超过了校准后技术.
- 取决于概率的梯度衰变影响了局部利普希茨约束.
结论:
- 梯度衰变率是Softmax中概括和校准的关键因素.
- 大梯度衰变为缓解神经网络过度自信提供了一个有希望的方法.
- 拟议的热身策略增强了培训稳定性和最终模型校准.
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