论文标题
一个简单的概率神经网络，用于机器理解
A simple probabilistic neural network for machine understanding
论文作者
论文摘要
我们讨论具有固定内部表示形式作为机器理解模型的概率神经网络。在这里，理解旨在将数据映射到已经存在的表示形式，该表示编码功能空间的{\ em先验组织。我们通过要求它满足最大相关性的原理和对不同特征的组合方式的最大无知的原则来得出内部表示。我们表明，当隐藏单元是二进制变量时，这两个原理确定了一个独特的模型 - 层次特征模型（HFM） - 它是完全可解决的，并在特征方面提供了自然的解释。我们认为，使用此体系结构的学习机享有许多有趣的属性，例如代表的连续性在参数和数据的变化方面，控制压缩水平以及支持超越概括的功能的能力的可能性。我们通过广泛的数值实验探索了模型的行为，并认为内部表示的模型固定了一种学习方式，该模式在质上与传统模型（例如受限的Boltzmann机器）质量不同。
We discuss probabilistic neural networks with a fixed internal representation as models for machine understanding. Here understanding is intended as mapping data to an already existing representation which encodes an {\em a priori} organisation of the feature space. We derive the internal representation by requiring that it satisfies the principles of maximal relevance and of maximal ignorance about how different features are combined. We show that, when hidden units are binary variables, these two principles identify a unique model -- the Hierarchical Feature Model (HFM) -- which is fully solvable and provides a natural interpretation in terms of features. We argue that learning machines with this architecture enjoy a number of interesting properties, like the continuity of the representation with respect to changes in parameters and data, the possibility to control the level of compression and the ability to support functions that go beyond generalisation. We explore the behaviour of the model with extensive numerical experiments and argue that models where the internal representation is fixed reproduce a learning modality which is qualitatively different from that of traditional models such as Restricted Boltzmann Machines.
