This document summarizes key concepts from Chapter 8 of the book "Pattern Recognition and Machine Learning" regarding probabilistic graphical models. It introduces directed and undirected graphical models as visualization tools for probabilistic relationships between random variables. It provides examples of Bayesian networks and conditional independence. Key points covered include using graphs to factorize joint probabilities, the d-separation criteria for identifying conditional independence based on a graph, and applying these concepts to linear Gaussian models and discrete variable models.
1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs.
2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs.
3. Another paper argues that classifiers can be viewed as joint energy-based models over inputs and outputs, and should be treated as such. It introduces a method to train classifiers as EBMs using contrastive divergence.
This chapter discusses approximate inference methods for probabilistic models where exact inference is intractable. It introduces variational inference as a deterministic approximation approach. Variational inference works by restricting the distribution of latent variables to a simpler family that makes computation and optimization easier. The chapter provides examples of using variational inference for Gaussian mixtures and univariate Gaussian models. It explains how to derive a variational lower bound and optimize it using an iterative procedure similar to EM.
1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs.
2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs.
3. Another paper argues that classifiers can be viewed as joint energy-based models over inputs and outputs, and should be treated as such. It introduces a method to train classifiers as EBMs using contrastive divergence.
This chapter discusses approximate inference methods for probabilistic models where exact inference is intractable. It introduces variational inference as a deterministic approximation approach. Variational inference works by restricting the distribution of latent variables to a simpler family that makes computation and optimization easier. The chapter provides examples of using variational inference for Gaussian mixtures and univariate Gaussian models. It explains how to derive a variational lower bound and optimize it using an iterative procedure similar to EM.
Vector Optimization (by Jinhwan Seok. M.S student at KAIST)
The concept of vector optimization and its applications
-Regularized least squares
-Smoothing approximation
-Reconstruction
Reference)
convex optimization, Boyd (2004)