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1
Introduction
2
Stochastic Block Model
3
Adjacency Matrix
4
SherringtonKirkpatrick Model
5
SherringtonKirkpatrick
6
Theorem
7
Random Graphs
8
Formula
9
Assumption
10
Geometric Interpretation
11
Algorithm
12
Algorithms
13
Algorithm structure
14
Two insights
15
Orthogonality
16
Optimization
Description:
Explore the optimization of the Sherrington-Kirkpatrick Hamiltonian in this 23-minute IEEE conference talk by Andrea Montanari. Delve into key concepts such as the Stochastic Block Model, Adjacency Matrix, and the Sherrington-Kirkpatrick Model and Theorem. Examine random graphs, formulas, and assumptions while gaining insights into the geometric interpretation of the problem. Learn about algorithm structures, including two crucial insights on orthogonality and optimization techniques, to enhance your understanding of this complex topic in statistical physics and machine learning.

Optimization of the Sherrington-Kirkpatrick Hamiltonian

IEEE
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