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Concept

Graph factorization

Related lectures (23)

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

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

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

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

Covers matrices, linear applications, vector spaces, and bijective functions.

Covers linear transformations, matrices, kernels, and properties of invertible matrices.

Explores vector space operations, linear transformations, matrix representation, and linear applications.

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

Covers the reduction of an application, finding reduced forms of matrices, diagonalizability, and polynomial roots.

Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

Covers the concept of graphical models and joint probability distributions.

Explores canonical basis, linear mapping, and eigenvalues in linear algebra.

Covers variational inference and neural networks for classification tasks.

Covers graphical models for probability distributions and factor graphs representation.

Explores the scalar product of vectors, including properties and calculation methods.

Covers translations, homotheties, and geometric transformations using vectors and matrices.

Covers the geometry of the triangle, including inequalities, heights, and centers.

Covers landmarks, coordinate systems, frames, and terminology in coordinates, emphasizing geometric angles and orthogonal vectors.

Explores projections on lines and symmetries in 2D space, emphasizing fixed points and symmetric matrices.

Introduces vector definitions, displacement, addition, and applications in geometry.