Concept

Belief propagation

Related lectures (12)

Covers belief propagation on graphs, exploring computation challenges and heuristics, focusing on sparse random graphs' loop properties.

Belief Propagation: Key Methods and Analysis

Covers Belief Propagation, a key method for both analysis and algorithm.

Generalized Linear Model: Optimization and Approximation

Discusses the unified formulation of the generalized linear model and the optimization of loss functions.

Belief Propagation and Survey Propagation

Explores belief propagation, frozen clusters, and colorability thresholds in graphical models, leading to the significance of survey propagation in solving constraint satisfaction problems.

Learning from Probabilistic Models

Delves into challenges of learning from probabilistic models, covering computational complexity, data reconstruction, and statistical gaps.

Graph Coloring: Theory and Applications

Explores graph coloring theory, spectral clustering, community detection, and network structures.

Noisy Gradient Descent Algorithms

Explores noisy gradient descent algorithms and their performance in high-dimensional optimization problems.

Belief Propagation

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.

Gaussian Acyclic Models: Linearity and Identifiability

Covers Gaussian Acyclic Models focusing on linearity and identifiability.

Network Inference from Textual Evidence

Delves into network inference from textual evidence, exploring information propagation, translation, and multi-input attention.

Committee Machine: Statistical Physics Approach

Explores hidden variables, graphical models, and computational gaps in neural network learning.

Cavity method and Approximate Message Passing

Explores the cavity method, Approximate Message Passing, and phase transitions in probabilistic models.