论文标题
印度厨师流程
The Indian Chefs Process
论文作者
论文摘要
本文介绍了印度厨师工艺（ICP），这是无限定向无环图（DAG）的关节空间上的贝叶斯非参数先验，以及概述印度自助餐过程的订单。正如我们的构造所示，所提出的分布依赖于控制节点的订单和传出连接概率的潜在β过程，并在稀疏的无限图上产生概率分布。 ICP比先前提出的DAG结构的贝叶斯非参数先验的主要优点是其更大的灵活性。据我们所知，ICP是第一个支持所有可能DAG的贝叶斯非参数模型。我们证明了ICP对学习深生成sigmoid网络以及卷积神经网络的结构的有用性。
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes Indian Buffet Processes. As our construction shows, the proposed distribution relies on a latent Beta Process controlling both the orders and outgoing connection probabilities of the nodes, and yields a probability distribution on sparse infinite graphs. The main advantage of the ICP over previously proposed Bayesian nonparametric priors for DAG structures is its greater flexibility. To the best of our knowledge, the ICP is the first Bayesian nonparametric model supporting every possible DAG. We demonstrate the usefulness of the ICP on learning the structure of deep generative sigmoid networks as well as convolutional neural networks.
