Course

PHYS-512: Statistical physics of computation

Lectures in this course (83)

Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.

Statistical Physics of Clusters

Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.

Generalized Linear Model: Optimization and Approximation

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

Gaussian Denoising: Statistical Physics Connection

Explores deriving key identities for statistical physics and their application in Gaussian denoising problems.

Committee Machine: Statistical Physics Approach

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

Phase Transitions in Signal Processing

Explores phase transitions in signal processing, demonstrating their impact on signal reconstruction and the application of threshold algorithms in image denoising.

Spin Glass Model: Replica Method

Explains the Replica Method for spin glass models and their factorization assumptions.

Binary Spiked Matrix Estimation

Explores binary spiked matrix estimation, analyzing consistent equations and Bayesian estimators.

Statistical Vision Problems

Explores statistical vision problems, evaluating algorithms for hidden signal estimation and the challenges of phase transitions.

Thales - Arnesson - Palmer: Part 2

Covers mathematical functions, inequalities, and geometric figures with practical applications and problem-solving strategies.

Belief propagation simplification

Explores simplifying belief propagation equations for pairwise models, reducing computational complexity from order n cubed to order n.

Stochastic Block Model: Community Detection

Covers the Stochastic Block Model for community detection, focusing on the detection of communities, clusters, and groups.

Message passing in networks

Explains message passing in networks, emphasizing probabilities and average edges between communities.

Magnetic Fields: Self-Adjusting Groups

Explores magnetic fields, self-adjusting groups, and particle suspension in practical applications.

Graph Coloring: Theory and Applications

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

Graph Coloring: Theory and Applications

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Satisfiability and clusters

Covers satisfiability threshold, clusters of solutions, parameter alpha, and average cluster computation.

Cluster Complexity Analysis

Covers the analysis of cluster complexity in random graph coloring.

Cluster entropy computation

Explains the computation of cluster entropy and the significance of sigma of s in the equation.

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.