论文标题
信念传播概括了反向传播
Belief propagation generalizes backpropagation
论文作者
论文摘要
人工智能中两个最重要的算法是反向传播和信仰传播。尽管它们的重要性，但它们之间的联系却很差。我们表明，当对反向传播的输入转换为信念传播的输入时，可以在其上运行（loopy）信仰传播，那么信仰传播的结果编码了反向传播的结果。因此，将反向传播恢复为一种信仰传播的特殊情况。换句话说，我们显然第一次证明信念传播概括了反向传播。我们的分析是理论上的贡献，我们希望它能调和我们对每种算法的理解，并作为寻求改善使用一种或另一个系统的系统行为的工程研究人员的指南。
The two most important algorithms in artificial intelligence are backpropagation and belief propagation. In spite of their importance, the connection between them is poorly characterized. We show that when an input to backpropagation is converted into an input to belief propagation so that (loopy) belief propagation can be run on it, then the result of belief propagation encodes the result of backpropagation; thus backpropagation is recovered as a special case of belief propagation. In other words, we prove for apparently the first time that belief propagation generalizes backpropagation. Our analysis is a theoretical contribution, which we motivate with the expectation that it might reconcile our understandings of each of these algorithms, and serve as a guide to engineering researchers seeking to improve the behavior of systems that use one or the other.
