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Jun, 2019
神经网络在概率测度空间和树形域上的逼近能力
Approximation capability of neural networks on spaces of probability measures and tree-structured domains
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Tomas Pevny, Vojtech Kovarik
TL;DR
该论文将神经网络的密度证明扩展到在概率密度函数紧致集合中的连续函数，进而给出了树形结构域的通用逼近定理，在结构化数据处理中具有重要的实际应用，这是AutoML范例的一个很好的例子。
Abstract
This paper extends the proof of density of
neural networks
in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of
probability measures
. By doing so the work
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