Graphs in Deep Learning: Applications and Techniques

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Related lectures (28)

Graphical Models: Probability Distributions and Factor Graphs

Covers graphical models for probability distributions and factor graphs representation.

Graphs and matrices

Explores graphs and matrices, including adjacency, degree, and Laplace matrices, Matrix-tree theorem, and spanning trees.

Pseudo Randomness in Graphs

Explores pseudo randomness in graphs using eigenvalues and polynomials, emphasizing the significance of bunched roots and common interlacers.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Understanding Surface Integrals

Explores surface integrals, emphasizing physical interpretation and mathematical calculations in vector fields and domains.

Mathematical Analysis: Functions and Composition

Covers the analysis of functions, composition, and mathematical induction.

Graph Neural Networks: Interconnected World

Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.

Learning from the Interconnected World with Graphs

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

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