Chapter 7. Energy-Based Models
Energy-based models are a broad class of generative model that borrow a key idea from modeling physical systems—namely, that the probability of an event can be expressed using a Boltzmann distribution, a specific function that normalizes a real-valued energy function between 0 and 1. This distribution was originally formulated in 1868 by Ludwig Boltzmann, who used it to describe gases in thermal equilibrium.
In this chapter, we will see how we can use this idea to train a generative model that can be used to produce images of handwritten digits. We will explore several new concepts, including contrastive divergence for training the EBM and Langevin dynamics for sampling.
Introduction
We will begin with a short story to illustrate the key concepts behind energy-based models.
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