Representation Learning: A Probabilistic Perspective
Spring 2020
Columbia University

Readings

Motivation

Background

Probabilistic PCA

Statistics and deep learning

Deep generative models

Posterior inference

Information theory and representation learning

Disentanglement and invariance

Embeddings