Pseudorandomness: Theory and Applications

Related lectures (29)

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Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

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

Explores interlacing families of polynomials and 1-sided Ramanujan graphs, focusing on their properties and construction methods.

Explores the construction of Ramanujan graphs using polynomials and addresses challenges with the probabilistic method.

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

Explores interlacing families, Ramanujan graphs, and their construction using signed adjacency matrices.

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

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

Explores automorphism groups of trees and graphs, including actions on trees and group homomorphisms.