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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44:29

11b

11b

From Luca Pesce

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44:30

11a

11a

From Luca Pesce

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46:50

TD5, Information theory

TD5, Information theory

From Guillaume Cyprien Dalle

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01:29:40

10, Random Constraint Satisfaction Problem

10, Random Constraint Satisfaction Problem

From Luca Pesce

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47:14

9b, Applications of GAMP

9b, Applications of GAMP

From Luca Pesce

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42:35

9a, GAMP

9a, GAMP

From Luca Pesce

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40:48

8b, Generalized Linearized Models

8b, Generalized Linearized Models

From Luca Pesce

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42:10

8a, A proof technique for the spiked model

8a, A proof technique for the spiked model

From Luca Pesce

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51:34

7b, Spiked Wigner model

7b, Spiked Wigner model

From Luca Pesce

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47:28

7a, AMP & State Evolution

7a, AMP & State Evolution

From Luca Pesce

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01:19:15

TD4b, Replica for the p-spin model

TD4b, Replica for the p-spin model

From Guillaume Cyprien Dalle

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19:22

TD4a, Maximum of Gaussians

TD4a, Maximum of Gaussians

From Guillaume Cyprien Dalle

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46:55

6b, TAP & Intro to AMP

6b, TAP & Intro to AMP

From Luca Pesce

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51:53

6a, Spin glass game

6a, Spin glass game

From Luca Pesce

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49:23

5b, Open problems

5b, Open problems

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