Key topics from other papers for energy disaggregation

VAE

• Perform maximum likelihood (ML) or maximum a posteriori (MAP) inference on (global) parameters and variational inference on the latent variables.
• When a neural network is used for the recognition model, we arrive at the variational auto-encoder.

• Interested in general algorithm that works efficiently in the case of Intractability (for the marginal likelihood p(x), p(x

  z) e.g neural nets with nonlinear hidden layer) and large dataset (to perform minibatch instead of batch or single point optimization).

• Three related problems solved: 1) ML estimation for parameters theta (note that this and marginal likelihood are different). 2) Approximate posterior inference of the latent variable z given x. 3) Approximate marginal inference of x
• We’ll introduce a method for learning the recognition model parameters phi jointly with the generative model parameters theta.
• Then it is obvious that L is a lower bound of the log probability of the observations. As a result, if in some cases we want to maximize the marginal probability, we can instead maximize its variational lower bound L. Variational Bound