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Lenka Zdeborová

Statistical Physics Studies of Machine Learning Problems:
> What makes problems studied in machine and statistical physics related? How can this relation be used to understand better the performance and limitations of machine learning systems? What happens when a phase transition is found in a computational problem? How do phase transitions influence algorithmic hardness?
[Using Physical Insights for ML](http://www.ipam.ucla.edu/programs/workshops/workshop-iv-using-physical-insights-for-machine-learning/?tab=overview)

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2 Documents (Long List)

- France is AI 2018: Lenka Zdeborova - Statistical physics modelling of machine learning - YouTube
*(About)*

> In data science, models are used to fit the data. In physics, models are the main tools for understanding

2019-08-15 - Cracking big data with statistical physics
*(About)*

> Models studied in statistical physics are mathematically equivalent to some of those in high-dimensional statistics. > Statistical physics is often concerned with phase transitions, i.e., abrupt changes in behaviour. Interestingly, there is a deep correspondence between physical phases such as liquid, super-cooled liquid or glass, and solid, and regions of parameters for which a given data analysis task is algorithmically impossible, hard or easy

2018-10-18

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- sl:creationDate : 2018-10-18
- sl:creationTime : 2018-10-18T13:27:16Z
- rdf:type : sl:Tag
- skos:prefLabel : Lenka Zdeborová
- foaf:homepage : http://artax.karlin.mff.cuni.cz/~zdebl9am/