玻尔兹曼机(也称为带有隐藏单元的随机Hopfield网络)是一种随机递归神经网络。这是一个马尔可夫随机场,它是从统计物理学翻译过来的,用于认知科学。Boltzmann机器基于具有外部场的随机旋转玻璃模型,即Sherrington-Kirkpatrick模型,它是随机的Ising模型,并应用于机器学习。Boltzmann机器可以看作是Hopfield网络的随机,生成对应物。它们是最早的能够学习内部表示的神经网络之一,并且能够表示和(给定足够的时间)解决组合问题。它是一类典型的随机神经网络属于反馈神经网络类型 。

最新论文

Restricted Boltzmann machines (RBM) and deep Boltzmann machines (DBM) are important models in machine learning, and recently found numerous applications in quantum many-body physics. We show that there are fundamental connections between them and tensor networks. In particular, we demonstrate that any RBM and DBM can be exactly represented as a two-dimensional tensor network. This representation gives an understanding of the expressive power of RBM and DBM using entanglement structures of the tensor networks, also provides an efficient tensor network contraction algorithm for the computing partition function of RBM and DBM. Using numerical experiments, we demonstrate that the proposed algorithm is much more accurate than the state-of-the-art machine learning methods in estimating the partition function of restricted Boltzmann machines and deep Boltzmann machines, and have potential applications in training deep Boltzmann machines for general machine learning tasks.

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