Deep Latent Variable Models
Ernie Chan (noreply@blogger.com)
In our previous blog post, we introduced latent variable models, where the latent variable can be thought of as a feature vector that has been “encoded” efficiently. This encoding turns the feature vector X into a context vector z. Latent variable models sound very GenAI-zy, but they descend from models that quant traders have long been familiar with.
No doubt you have heard of PCA or SVD (see Chapter 3 of our book for a primer)? Principal components or singular vectors are ways to represent r