Rbmenergilagring

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What is a general RBM?

The general RBM provides a powerful tool for representing dependency structure between random variables. Recently, the RBM has drawn great interest of the artificial intelligence and machine learning community. The RBM has been developed mainly for classification and representational learning.

What are the major improvements in RBM?

The major improvements on the RBM can be divided into four aspects: stochastic approximation, regularization, energy function and objective function. The Contrastive Divergence (CD) and the Persistent Contrastive Divergence (PCD) are common stochastic approximation methods in the RBM and RBM variants.

What are regularization methods in RBM?

Dropout, dropconnect and weight uncertainty are common regularization methods in the RBM. In the RBM, the key idea of the dropout is to randomly drop hidden units from the neural network during training. The dropout can prevent the visible units of the RBM becoming dependent on any one of hidden units.

What are RBM variants?

In order to deal with real images, a series of RBM variants are put forward, such as the Gaussian-Binary RBM (GRBM) , , , the covariance RBM (cRBM) , the mean and covariance RBM (mcRBM) , and the spike-and-slab RBM (ssRBM) , , , , .

What is robust RBM & pgrbm?

The Robust RBM (RoBM) and the Point-wise Gated RBM (pgRBM) are two RBM variants which adjust the network model and the energy function of the RBM to address images containing irrelevant patterns , .

Why is the RBM important?

The RBM provides a powerful tool for learning useful representations of the data. Due to its flexibility, theories and applications of the RBM have been extensively studied. The previous two sections cover the general RBM and the improvements on the RBM.