PHYS-642 Statistical physics for optimization & learning 23

PHYS-642 Statistical physics for optimization & learning 23

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This course covers the statistical physics approach to computer science problems, with an emphasis on heuristic & rigorous mathematical technics, ranging from graph theory and constraint satisfaction to inference for machine learning, neural networks and statistics.

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01:37:28

1. Curie Weiss Model

1. Curie Weiss Model

From Matteo Vilucchio

1 likes 205 plays 0
In this lecture, the main focus is studying the Curie-Weiss model. The topics are: Introduction to Statistical Physics Computation of the probability of having a…
01:29:40

10, Random Constraint Satisfaction Problem

10, Random Constraint Satisfaction Problem

From Luca Pesce

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44:30

11a

11a

From Luca Pesce

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44:29

11b

11b

From Luca Pesce

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45:20

2a, RFIM - Variational method

2a, RFIM - Variational method

From Luca Pesce

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47:38

2b, RFIM - Cavity and Replica method

2b, RFIM - Cavity and Replica method

From Luca Pesce

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48:05

3a, RFIM on a tree & BP

3a, RFIM on a tree & BP

From Luca Pesce

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51:35

3b, RFIM on sparse graphs & Population…

3b, RFIM on sparse graphs & Population dynamics

From Luca Pesce

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52:41

4a, Random Energy Model - Full solution &…

4a, Random Energy Model - Full solution & Condensation

From Luca Pesce

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49:07

4b, REM - Replica Symmetry Breaking

4b, REM - Replica Symmetry Breaking

From Luca Pesce

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51:26

5a, Graphical models

5a, Graphical models

From Luca Pesce

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49:23

5b, Open problems

5b, Open problems

From Luca Pesce

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51:53

6a, Spin glass game

6a, Spin glass game

From Luca Pesce

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46:55

6b, TAP & Intro to AMP

6b, TAP & Intro to AMP

From Luca Pesce

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47:28

7a, AMP & State Evolution

7a, AMP & State Evolution

From Luca Pesce

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29 Media

7 Members

Managers:: Florent Krzakala
Luca Pesce
Lenka Zdeborová
Matteo Vilucchio
Luca Arnaboldi
Guillaume Cyprien Dalle
Appears In:: EPFL courses