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Aug, 2017
近临临界状态下的 Ising 模型深度学习
Deep Learning the Ising Model Near Criticality
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Alan Morningstar, Roger G. Melko
TL;DR
使用深度玻尔兹曼机、深度置信网络和深度限制玻尔兹曼网络对二维 Ising 系统进行非监督生成建模,比较与浅层架构的有限玻尔兹曼机的效果,并发现只有第一隐藏层的神经元数量对于生成能量观测量的准确度有影响,而架构的深度和模型类型对于准确度的影响很小,这证明在表示与 Ising 系统的临界概率分布相关的物理概率分布时,浅层网络比深度神经网络更有效率。
Abstract
It is well established that
neural networks
with deep architectures perform better than shallow networks for many tasks in machine learning. In statistical physics, while there has been recent interest in representing physical data with
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