论文标题
氛围是半贝叶斯
VIB is Half Bayes
论文作者
论文摘要
在歧视性设置（例如回归和分类）中，有两个随机变量正在玩，输入X和目标Y。在这里，我们证明了变异信息瓶颈可以看作是完全经验和完全贝叶斯目标之间的妥协，试图最大程度地减少Y的风险。我们认为这种方法提供了贝叶斯的一些好处，同时仅需要一些工作。
In discriminative settings such as regression and classification there are two random variables at play, the inputs X and the targets Y. Here, we demonstrate that the Variational Information Bottleneck can be viewed as a compromise between fully empirical and fully Bayesian objectives, attempting to minimize the risks due to finite sampling of Y only. We argue that this approach provides some of the benefits of Bayes while requiring only some of the work.
