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Related lectures (14)

Graphical Models: Probability Distributions and Factor Graphs

Covers graphical models for probability distributions and factor graphs representation.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Graphical models: Inference and Factor Graphs

Explores graphical models, factor graphs, and probabilistic inferences in complex systems.

Graphical Models: Joint Probability Distribution

Covers the concept of graphical models and joint probability distributions.

Robust State Estimation: ROSE

Discusses the ROSE framework for robust state estimation and multi-modal sensor fusion.

Learning from Probabilistic Models

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

Belief Propagation

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

Statistical Physics of Computation: Insights and Applications

Explores the application of statistical physics in computational problems, covering topics such as Bayesian inference, mean-field spin glass models, and compressed sensing.

Noisy Gradient Descent Algorithms

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

Handling Network Data

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Belief Propagation: Key Methods and Analysis

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

Committee Machine: Statistical Physics Approach

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

Generalized Linear Model: Optimization and Approximation

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

Cavity method and Approximate Message Passing

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

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