Two R\'{e}nyi-type generalizations of the Shannon cross-entropy, the R\'{e}nyi cross-entropy and the Natural R\'{e}nyi 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 R\'{e}nyi and Natural R\'{e}nyi 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 R\'{e}nyi-type cross-entropy rates between stationary Gaussian processes and between finite-alphabet time-invariant Markov sources.
翻译：R\'{{{{{{{{{{{{{{{{{}}}对香农交叉恋、R\{{{{{{{{}}}交叉恋和自然R\{{{{{{{{{}}}交叉恋的两种类型典型的概括性概括性做法最近被用作改进深学习基因对抗网络设计的损失函数。在这项工作中,我们以我们[1]的结果为基础,为属于指数式家族的多种常见连续分布而采用封闭式的R\'{{{{{{{{{{{{{{{{{{}但有差异性交叉恋性措施,并将结果制表列以方便参考。我们还总结了固定的高斯进程之间和固定式高斯进程之间以及固定式高斯马可变时间源间之间的R\{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{}马可度源之间,我们利用我们以[1]在[1]基于我们的[1]结果的基础上,得出我们的[1]结果,为封闭式的封闭式的跨体,为封闭式的跨式,对属于指数式,对属于指数式的多种共连续分布式的多变数源源。
相关内容
微信扫码咨询专知VIP会员