Graph Models and Brain Connectomics

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Related lectures (32)

Neuronal Connectivity Patterns Log in to Mediaspace to watch this video

Explores neuronal connectivity patterns, connection probabilities, and experimental techniques used to study synaptic connectivity.

Sparsest Cut: ARV Theorem Log in to Mediaspace to watch this video

Covers the proof of the Bourgain's ARV Theorem, focusing on the finite set of points in a semi-metric space and the application of the ARV algorithm to find the sparsest cut in a graph.

Network clustering Log in to Mediaspace to watch this video

Explores network clustering, spectral clustering, k-means algorithm, eigenvalue properties, block model estimation, and structural similarity measurement.

Statistical Analysis of Network Data Log in to Mediaspace to watch this video

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

Convergence of Random Walks Log in to Mediaspace to watch this video

Explores the convergence of random walks on graphs and the properties of weighted adjacency matrices.

Learning from the Interconnected World with Graphs Log in to Mediaspace to watch this video

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

Graph Theory Fundamentals Log in to Mediaspace to watch this video

Explores fundamental graph theory concepts, Erdős' results, Chromatic Lemma, and Union Bound theorem in graph theory.

Interlacing Families and Ramanujan Graphs Log in to Mediaspace to watch this video

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

Sparsest Cut and Concurrent Flow Log in to Mediaspace to watch this video

Covers sparsest cut, NP-completeness, Bougains Theorem, and concurrent flow in graphs.

Relations Between Events Log in to Mediaspace to watch this video

Explores relations between events, disjunctive constraints, and modeling with binary variables in optimization problems.

Minimum Spanning Trees Log in to Mediaspace to watch this video

Covers the implementation and analysis of disjoint sets data structure and introduces the concept of minimum spanning trees.

Neural networks under SGD Log in to Mediaspace to watch this video

Explores the optimization of neural networks using Stochastic Gradient Descent (SGD) and the concept of dual risk versus empirical risk.

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