Extracting stochastic machines from recurrent neural networks trained on complex symbolic sequences

P. Tiňo, V. Vojtek
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引用次数: 13

Abstract

We train a recurrent neural network on a single, long, complex symbolic sequence with positive entropy. The training process is monitored through information theory based performance measures. We show that although the sequence is unpredictable, the network is able to code the sequence's topological and statistical structure in recurrent neuron activation scenarios. Such scenarios can be compactly represented through stochastic machines extracted from the trained network. Generative models, i.e. trained recurrent networks and extracted stochastic machines, are compared using entropy spectra of generated sequences. In addition, entropy spectra computed directly from the machines capture generalization abilities of extracted machines and are related to a machines' long term behavior.
从复杂符号序列训练的递归神经网络中提取随机机器
我们训练一个循环神经网络在一个单一的,长,复杂的符号序列与正熵。培训过程通过基于信息论的绩效测量进行监控。我们表明,尽管序列是不可预测的,但网络能够在循环神经元激活场景中编码序列的拓扑和统计结构。这些场景可以通过从训练网络中提取的随机机器来紧凑地表示。生成模型,即训练的循环网络和提取的随机机器,使用生成序列的熵谱进行比较。此外,从机器直接计算的熵谱捕获了提取机器的泛化能力,并与机器的长期行为有关。
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