寻求利用深度学习中的记忆效应,使用噪音标签
概括
这项研究引入了一种新的双层优化方法,用于从噪音标签中进行强有力的学习. 该方法有效地控制样本选择,提高深度网络性能,优于现有的自动化机器学习技术.
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.
- 计算机科学 计算机科学
背景情况:
- 对于深度网络来说,从噪音标签中进行强有力的学习至关重要.
- 控制样本选择以利用记忆效应仍然具有挑战性.
- 自动机器学习 (AutoML) 在相关领域取得了成功.
研究的目的:
- 提出一种新的双层优化框架,用于控制强大的学习中的样本选择.
- 通过有效利用记忆效应来提高深度网络性能.
- 开发一种有效的方法,以找到最佳的样本选择时间表.
主要方法:
- 采用双层优化策略,在上层对选择过程进行参数化,并在下层通过模型培训进行优化.
- 集成了半监督学习算法,以利用有噪音标记的数据作为未标记的数据.
- 两级优化问题是使用牛顿和立方正规化方法解决的,考虑到验证曲率.
主要成果:
- 牛顿和立方正规化方法都汇聚到近似的静止点.
- 立方正规化在减少计算时间的情况下找到更好的局部最佳值方面表现出卓越的性能.
- 拟议的方法在基准和现实世界数据集上取得了明显的改进,而不是现有的技术.
结论:
- 拟议的双层优化方法有效地控制了从噪音标签中进行强有力的学习的样本选择.
- 立方正规化方法为优化问题提供了高效有效的解决方案.
- 这项研究为优化样本选择时间表提供了比现有的AutoML方法更有效的替代方案.
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