论文标题
基于区域的能量神经网络用于近似推断
Region-based Energy Neural Network for Approximate Inference
论文作者
论文摘要
最初提出了基于区域的自由能，用于普遍的信念传播（GBP），以改善循环信念传播（Loopy BP）。在本文中，我们提出了一个基于神经网络的能量模型，用于一般的马尔可夫随机场（MRF），该模型直接最大程度地减少了在区域图上定义的基于区域的自由能。我们称我们的基于模型区域的能量神经网络（RENN）。与消息通讯算法不同，Renn避免了迭代消息传播，并且更快。与最近的基于Deep Newer网络的模型不同，RENN的推断不需要采样，Renn在一般MRF上工作。 Renn也可以用于MRF学习。我们对MRF的边际分布估计，分区函数估计和学习的实验表明，Renn的表现优于平均场方法，Loopy BP，GBP和最新的基于神经网络的模型。
Region-based free energy was originally proposed for generalized belief propagation (GBP) to improve loopy belief propagation (loopy BP). In this paper, we propose a neural network based energy model for inference in general Markov random fields (MRFs), which directly minimizes the region-based free energy defined on region graphs. We term our model Region-based Energy Neural Network (RENN). Unlike message-passing algorithms, RENN avoids iterative message propagation and is faster. Also different from recent deep neural network based models, inference by RENN does not require sampling, and RENN works on general MRFs. RENN can also be employed for MRF learning. Our experiments on marginal distribution estimation, partition function estimation, and learning of MRFs show that RENN outperforms the mean field method, loopy BP, GBP, and the state-of-the-art neural network based model.
