While widely adopted, contrastive divergence methods for Restricted Boltzmann Machines typically result in poor representations of the data distribution. Here, the authors propose an unsupervised training where gradient-descent is combined with the Machine’s mode samples, significantly improving the final model quality.
- Haik Manukian
- Yan Ru Pei
- Massimiliano Di Ventra
