Boltzmann Machines
Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? Simplest approach to generative deep learning.
- Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.
- A generative model can learn to represent and sample from a probability distribution. The core idea is to learn a parametric model of the probability distribution from which the training data was drawn. As an example
- A model for images could learn to draw new examples of cats and dogs, given a training dataset of images of cats and dogs.
- Generate a sample of an ordered or disordered phase, having been given samples of such phases.
- Model the trial function for Monte Carlo calculations.