论文标题
可逆低差异编码
Invertible Low-Divergence Coding
论文作者
论文摘要
交流，控制和学习中的几个应用需要将目标分布近似于小信息差异（i-Divergence）。不可逆转的附加要求通常会导致使用一对一映射的编码器，也称为分销匹配器。但是，即使是最好的一对一编码器也具有i-Diverence，它们通常会随块长度而对数。为了提高性能，提出了一个编码器，该编码器具有可逆的一对多映射和低率分辨率代码。开发了两种算法来设计映射，通过在最可能的第一阶或最不可能的一阶中分配字符串。两种算法都给出了接近目标分布的熵的信息速率，并以指数降低I-Divergence，并且在块长度中消失的分辨率。
Several applications in communication, control, and learning require approximating target distributions to within small informational divergence (I-divergence). The additional requirement of invertibility usually leads to using encoders that are one-to-one mappings, also known as distribution matchers. However, even the best one-to-one encoders have I-divergences that grow logarithmically with the block length in general. To improve performance, an encoder is proposed that has an invertible one-to-many mapping and a low-rate resolution code. Two algorithms are developed to design the mapping by assigning strings in either a most-likely first or least-likely first order. Both algorithms give information rates approaching the entropy of the target distribution with exponentially decreasing I-divergence and with vanishing resolution rate in the block length.
