论文标题
树木：与树近似消息传递的组成推断
Tree-AMP: Compositional Inference with Tree Approximate Message Passing
论文作者
论文摘要
我们介绍树木，代表树近似消息传递，这是一个用于高维树结构模型中的构图推断的Python软件包。该软件包提供了一个统一的框架，可以研究以前针对各种机器学习任务的几个近似消息传递算法，例如通用线性模型，多层网络中的推断，矩阵分解以及使用不可分离的惩罚进行重建。对于某些模型，理论上可以通过状态进化来预测该算法的渐近性能，以及由自由熵形式主义估计的测量熵。实现是按设计模块化的：每个模块都可以随意与其他模块一起求解复杂的推理任务。用户只需要声明模型的因子图：推理算法，状态进化和熵估计是完全自动化的。
We introduce Tree-AMP, standing for Tree Approximate Message Passing, a python package for compositional inference in high-dimensional tree-structured models. The package provides a unifying framework to study several approximate message passing algorithms previously derived for a variety of machine learning tasks such as generalized linear models, inference in multi-layer networks, matrix factorization, and reconstruction using non-separable penalties. For some models, the asymptotic performance of the algorithm can be theoretically predicted by the state evolution, and the measurements entropy estimated by the free entropy formalism. The implementation is modular by design: each module, which implements a factor, can be composed at will with other modules to solve complex inference tasks. The user only needs to declare the factor graph of the model: the inference algorithm, state evolution and entropy estimation are fully automated.
