相关实验视频
Updated: Mar 3, 2026

13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
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一种混合适应式预先条件的梯度方法,具有深度学习的动力
Zhiyang Zhou1, Huisheng Zhang2, Zhaoyang Chen2
1College of Artificial Intelligence, Dalian Maritime University, Dalian, 116026, China.
概括
我们介绍了AdapGradm,这是一款用于深度学习的新型二阶优化器,与一阶效率相匹配. 它的混合版本,HAdapGradm,表现出比Adam更好的训练错误和概括.
科学领域:
- 机器学习 机器学习
- 深度学习优化优化
背景情况:
- 深度神经网络通常使用一阶随机优化.
- 二级方法提供更快的融合,但在深度学习中面临计算开销.
研究的目的:
- 介绍AdapGradm,这是一款用于深度学习的新型二级自适应优化器.
- 提出HAdapGradm,这是一个混合优化器,可以在AdapGradm和SGD之间无过渡.
- 评估这些新优化器的性能和融合.
主要方法:
- 为了提高效率,AdapGradm使用了从一级衍生品获得的对角近似赫西安.
- HAdapGradm将AdapGradm与SGD结合在一起,以实现灵活的优化.
- 在温和条件下,收是严格确定的.
主要成果:
- AdapGradm实现了与Adam等一级优化器可比的计算效率.
- 与Adam和基线优化器相比,HAdapGradm表现出较低的训练错误.
- HAdapGradm在图像分类和NLP任务中表现出卓越的概括能力.
结论:
- AdapGradm和HAdapGradm为深度学习提供了高效的二级优化.
- HAdapGradm为现有优化器提供了一种实用且有效的替代方案.
- 提出的方法提升了深度学习培训的效率和性能.
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