论文标题
带有记忆的共同分布和过程的Rényi跨透明镜头测量
Rényi Cross-Entropy Measures for Common Distributions and Processes with Memory
论文作者
论文摘要
最近将两种Rényi-Type概括性化的横向概括，Rényi跨透明镜和天然的Rényi跨透明拷贝，最近用作改进深度学习生成的对抗网络设计的损失函数。在这项工作中，我们在[1]中基于结果，以封闭形式得出Rényi和自然的Rényi差异差透明镜测量，用于属于指数家族的各种常见连续分布，并制表结果，以易于参考。我们还总结了固定高斯工艺之间以及有限的阿尔如图时间不变的马尔可夫来源之间的rényi-type跨膜片速率。
Two Rényi-type generalizations of the Shannon cross-entropy, the Rényi cross-entropy and the Natural Rényi cross-entropy, were recently used as loss functions for the improved design of deep learning generative adversarial networks. In this work, we build upon our results in [1] by deriving the Rényi and Natural Rényi differential cross-entropy measures in closed form for a wide class of common continuous distributions belonging to the exponential family and tabulating the results for ease of reference. We also summarise the Rényi-type cross-entropy rates between stationary Gaussian processes and between finite-alphabet time-invariant Markov sources.
