学习学习:如何持续教导人类和机器
Parantak Singh1,2, You Li2,3, Ankur Sikarwar1,2
1Nanyang Technological University (NTU), Singapore.
概括
优化学习序列或课程,可以显著改善人类和机器在持续学习环境中的知识转移. 对人类有效的课程与对机器学习算法有益的课程有很强的相关性.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 机器学习 机器学习
背景情况:
- 课程设计对于教育中的有效知识转移至关重要.
- 之前关于课程设计的研究主要集中在单一的离线任务上.
- 在线课程增量持续学习设置涉及连续学习任务.
研究的目的:
- 调查任务顺序对在线课堂增量持续学习中的学习成果的影响.
- 为了比较课程对人类和机器学习算法的有效性.
- 开发一个自动化算法来设计有效的课程.
主要方法:
- 研究了任务顺序在在线课堂增量持续学习中的影响.
- 介绍了一个新的对象识别数据集用于人类实验.
- 开发并评估了基于跨类特征相似性的课程设计 (CD) 算法.
主要成果:
- 课程顺序显著影响人类和机器学习算法的学习结果.
- 对人类有效的课程与对机器有效的课程高度相关.
- 拟议的CD算法显示了与经验有效的课程有显著的重叠.
结论:
- 优化课程增强知识的转移,并尽量减少忘记在持续学习.
- 建立了一个统一的框架来研究和设计人类和机器的课程.
- 针对在线持续学习的自动化课程设计是一个有前途的研究方向.
相关概念视频
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