Sign inSign up
Data science and the curse of phase transitions thumbnail
Pause
Mute
Subtitles
Playback speed
0.25
0.5
0.75
1
1.25
1.5
1.75
2
Full screen

Data science and the curse of phase transitions

Published on Nov 28, 20162344 Views

Marc Mézard

Extracting information, and more generally extracting knowledge from large datasets is arguably one of the main frontiers of modern science, common to a broad variety of disciplines. Bayesian approach

BIDSA Inaugural Conference 2016 - Milan

Related categories

PhysicsEconomicsComputer ScienceData ScienceMathematicsStatistics

Chapter list

Data science and the curse of phase transitions00:00
Information Theory00:19
More is different01:32
Ferromagnetism03:00
Emergence = collective behavior - 104:22
Emergence = collective behavior - 205:15
Phase transition with many states: spin glases - 105:27
Phase transition with many states: spin glases - 207:52
Phase transition with many states: spin glases - 308:13
Phase transition with many states: spin glases - 408:29
The spin glass cornucopia09:15
Why? - 110:06
Why? - 210:40
Phase Transitions in Information Theory10:48
Information transfer - 111:18
Information transfer - 211:50
Error correction12:15
Shannon's phase transition - 115:03
Shannon's phase transition - 216:57
Shannon's phase transition - 317:36
Shannon's phase transition - 418:44
Efficient codes21:34
Error decoding: inference problem - 122:36
Error decoding: inference problem - 225:11
More Phase Transitions in Error correcting codes25:58
Phase Transitions in Information theory27:23
Compressed sensing27:31
Compressed sensing motivation29:27
Tomography29:53
An example: tomography of binary mixtures - 130:26
An example: tomography of binary mixtures - 230:44
An example: tomography of binary mixtures - 332:15
An example: tomography of binary mixtures - 433:01
An example: tomography of binary mixtures - 533:14
An example: tomography of binary mixtures - 633:52
Compressed sensing as an optimization problem35:01
phase diagram - phase transitions35:34
Alternative approach36:25
Bayesian approach36:50
Analysis of random instances - 137:22
Analysis of random instances - 237:25
Seeding the crystal37:49
Nucleation and seeding - 137:51
Nucleation and seeding - 238:13
Nucleation and seeding - 338:24
Nucleation and seeding - 438:34