学习概念 可信的模型,以缓解快捷方式
Jiaxuan Wang1, Sarah Jabbour1, Maggie Makar1
1Division of Computer Science & Engineering, University of Michigan, Ann Arbor, MI, USA.
Advances in neural information processing systems
|December 27, 2023
概括
本研究引入了新的方法来训练强大的机器学习模型,通过利用领域知识和学习未知的概念,减轻偏见数据的捷径学习.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 模型在训练过程中经常学习虚假的相关性 (快捷方式),导致对新数据的概括性差.
- 当快捷方式与未知的概念联系在一起或仅依赖预定义的知识时,现有的方法会遇到困难.
研究的目的:
- 通过减轻捷径学习,从有偏见的培训数据中开发 robust 和准确的学习模型的方法.
- 整合领域知识 (已知的概念),同时允许模型学习未知的概念.
主要方法:
- 一个两阶段的方法:首先将模型与已知的概念相匹配,然后处理未知概念的残余.
- 一种扩展的规范化处罚方法,将已知的和未知的概念整合起来,以防止捷径利用.
主要成果:
- 两个阶段的方法显示当快捷方式与未知的概念相关时,脆弱性.
- 扩展的规范化惩罚方法有效地减轻了两种现实世界数据集之间的捷径学习.
结论:
- 两种拟议的方法都成功地减少了在受过偏差数据训练的模型中的捷径学习.
- 将领域知识与学习未知的概念的能力相结合,对于强大的模型概括至关重要.
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