论文标题
由深层计算机计算的功能空间
Space of Functions Computed by Deep-Layered Machines
论文作者
论文摘要
我们研究由随机层机计算的功能空间，包括深神经网络和布尔电路。研究在复发和层依赖性体系结构上计算出的布尔函数的分布，我们发现在这两个模型中都是相同的。根据所使用的初始条件和计算元素的不同，我们表征了在大深度极限下计算的功能空间，并表明布尔函数的宏观熵可以随着增长深度而单调增加或减小。
We study the space of functions computed by random-layered machines, including deep neural networks and Boolean circuits. Investigating the distribution of Boolean functions computed on the recurrent and layer-dependent architectures, we find that it is the same in both models. Depending on the initial conditions and computing elements used, we characterize the space of functions computed at the large depth limit and show that the macroscopic entropy of Boolean functions is either monotonically increasing or decreasing with the growing depth.
