Lectures related to Cubic graph

Graphical models: Inference and Factor Graphs

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

Algorithmic Paradigms for Dynamic Graph Problems

Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.

Graphical Models: Probability Distributions and Factor Graphs

Covers graphical models for probability distributions and factor graphs representation.

Graph-to-Graph Transformers: Syntax-aware Graph Encoding

Introduces the Syntax-aware Graph-to-Graph Transformer architecture for effective conditioning on syntactic dependency graphs.

Bipartite and n-partite Graphs

Covers bipartite and n-partite graphs, including complete graph definitions and examples.

Robust State Estimation: ROSE

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

Graph Surfaces and Sphere in Spherical Coordinates

Covers graph surfaces with an example of a surface defined by a parametric equation.

Graph Coloring: Random vs Symmetrical

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.

Cluster Complexity Analysis

Covers the analysis of cluster complexity in random graph coloring.

Statistical Physics of Clusters

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

Graphical Models: Joint Probability Distribution

Covers the concept of graphical models and joint probability distributions.

Graphical Models: Representing Probabilistic Distributions

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

Isomorphic Graphs: Definition and Examples

Explains isomorphic graphs through examples and the relationship with identical graphs.

Belief Propagation

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

Graph Surfaces: Integration and Vector Calculus

Covers integrating functions over graph surfaces in vector calculus, emphasizing the interpretation of divergence theorem and special cases of domain between two graphs.

Bethe Free Entropy

Covers the computation of Bethe free entropy and the interpretation of messages between variables and factors.

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Graph Coloring: Basics and Applications

Covers the basics and applications of graph coloring, including balancing vectors and achieving perfect fairness.

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Tangent Lines and Approximation

Covers the concept of tangent lines and function approximation near a point.