  Energy-Based Models (EBMs), also known as non-normalized probabilistic
models, specify probability density or mass functions up to an unknown
normalizing constant. Unlike most other probabilistic models, EBMs do not place
a restriction on the tractability of the normalizing constant, thus are more
flexible to parameterize and can model a more expressive family of probability
distributions. However, the unknown normalizing constant of EBMs makes training
particularly difficult. Our goal is to provide a friendly introduction to
modern approaches for EBM training. We start by explaining maximum likelihood
training with Markov chain Monte Carlo (MCMC), and proceed to elaborate on
MCMC-free approaches, including Score Matching (SM) and Noise Constrastive
Estimation (NCE). We highlight theoretical connections among these three
approaches, and end with a brief survey on alternative training methods, which
are still under active research. Our tutorial is targeted at an audience with
basic understanding of generative models who want to apply EBMs or start a
research project in this direction.
