雷尼对常见分布和具有记忆力的过程的交叉度测量
Ferenc Cole Thierrin1, Fady Alajaji1, Tamás Linder1
1Department of Mathematics and Statistics, Queen's University, Kingston, ON K7L 3N6, Canada.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
这项研究为指数式家族分布推导了封闭形式的Rényi和自然Rényi微分交叉. 这些措施对于改善深度学习生成对抗网络和分析信息速率至关重要.
科学领域:
- 信息理论 信息理论
- 机器学习 机器学习
- 统计推理 统计推理
背景情况:
- 香农交叉是信息理论的一个基本概念.
- 雷尼类型的交叉为深度学习中的应用提供了概括.
- 生成对抗网络 (GANs) 从改进的损失函数中受益.
研究的目的:
- 为雷尼和自然雷尼微分交叉导出封闭形式的表达式.
- 为了分析这些指标,在指数家族中分析常见的连续分布.
- 为了总结高斯过程和马尔科夫源的交叉率.
主要方法:
- 对差异交叉度测量的分析表达式的导数.
- 对指数式家族分布的结果的表化.
- 对于特定的随机过程的交叉率的总结.
主要成果:
- 对于雷尼和自然雷尼差异交叉的封闭形式解决方案被介绍.
- 为广泛的指数家族分布类提供了结果.
- 对于静止高斯过程和有限字母马尔科夫源的交叉率进行了总结.
结论:
- 由此产生的指标为深度学习应用提供了有价值的工具,特别是GANs.
- 表格化结果有助于实际应用和不同分布的比较.
- 该研究增强了对复杂系统中的信息理论速率的理解.
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