精确和软的信息瓶的连续改进信息瓶
Hippolyte Charvin1, Nicola Catenacci Volpi1, Daniel Polani1
1School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
适应性系统可以利用现有的最佳表示方式来实现新的最佳表示方式. 本研究研究了信息瓶 (IB) 框架内的连续改进,在多阶段处理中发现了最小的信息损失.
科学领域:
- 信息理论 信息理论
- 机器学习 机器学习
- 计算神经科学是一种神经科学.
背景情况:
- 信息瓶 (IB) 框架平衡了表示复杂性和信息提取.
- 现实世界的系统需要随着时间的推移而变化的复杂度的可适应的表示.
- 更新表示的成本对于有效的信息处理至关重要.
研究的目的:
- 调查自适应系统如何能够重复使用现有的IB-最佳表示,以获得不同细分度的新表示.
- 探索适应IB-最佳表示的信息理论极限.
- 在IB框架内扩大连续改进的概念.
主要方法:
- 在IB框架内研究和扩展了连续改进的概念.
- 开发了一种用于分析导数的新几何特征.
- 提供了基于线性编程的工具,用于在离散情况下进行数值调查.
- 量化信息的最佳性损失,使用单一信息的量度.
主要成果:
- 对特定IB问题 (二进制,联合高斯函数,确定性函数) 进行分析推导的连续精制性.
- 开发了一个数值工具,用于在离散环境中调查IB的连续改进.
- 在多阶段处理中量化信息的最佳性损失,发现它通常很低,但不可忽视.
结论:
- 适应性系统可以有效地利用现有的IB-最佳表征来实现新的表征.
- 连续的改进提供了一个理论上的限制,以适应最小的信息损失的表示细分度.
- 结果对增量学习,统计决策问题和深度神经网络理论有影响.
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