Before revision:  This represents an improvement when s < \log_2 (d-1), which is satisfied by many classes of RBMs with "few latent variables'' . Furthermore, we give a version of this learning algorithm that recovers a model with small prediction error and whose sample complexity is independent of the minimum potential in the Markov Random Field of the observed variables.
After revision:  This represents an improvement when s < \log_2 (d-1), which corresponds to RBMs with sparse latent variables . Furthermore, we give a version of this learning algorithm that recovers a model with small prediction error and whose sample complexity is independent of the minimum potential in the Markov Random Field of the observed variables.