The paper uses a subsampling-based method to speed up ratio matching training of 
RBMs on high-dimensional sparse binary data. The proposed approach is a simple 
adaptation of the method proposed by Dauphin et al. (2011) for denoising 
autoencoders. 

This paper develops an algorithm that can successfully train RBMs on very high dimensional but sparse input data, such as often arises in NLP problems. The algorithm adapts a previous method developed for denoising autoencoders for use with RBMs. The authors present extensive experimental results verifying that their method learns a good generative model; provides unbiased gradient estimates; attains a two order of magnitude speed up on large sparse problems relative to the standard implementation; and yields state of the art performance on a number of NLP tasks. They also document the curious result that using a biased version of their estimator in fact leads to better performance on the classification tasks they tested.