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[Fizinfo] Wigner FK RMI Elméleti Osztály Szemináriuma


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  • From: Balog Janos <balog.janos AT wigner.mta.hu>
  • To: fizinfo AT lists.kfki.hu, rmkiusers AT lists.kfki.hu
  • Subject: [Fizinfo] Wigner FK RMI Elméleti Osztály Szemináriuma
  • Date: Sat, 22 Jan 2022 16:45:55 +0100
  • Authentication-results: smtp0.kfki.hu (amavisd-new); dkim=pass (1024-bit key) reason="pass (just generated, assumed good)" header.d=wigner.mta.hu

Wigner FK RMI Elméleti Osztály Szemináriuma
Tisztelettel meghívjuk


Romuald Janik
(Krakow)


"From Machine Learning to Physics, and back again..."


címmel tartandó hibrid szemináriumára.

Az előadás linkje:


https://wigner-hu.zoom.us/j/91382490019?pwd=Z3dncSsycjhWRHBVcUMzNlpMWGd2Zz09

Meeting ID: 913 8249 0019
Passcode: 463524


Kivonat:

In this talk I would like to describe some fruitful interrelations between Machine Learning and Physics.
On the one hand, I will show how to use the tools of machine learning to estimate the entropy (and free energy) of a system directly from Monte Carlo configurations at a given temperature, which is commonly believed to be extremely difficult if not impossible by conventional means.
On the other hand, I will describe a proposed definition of complexity for deep neural networks, which is based on some intuitions from physics. I will show how one can use it together with a complementary notion of effective dimension to quantify the intuitive difficulty of a dataset or a learning task. These notions also reveal a rather mysterious power-law scaling during training.

Helye: RMI 2. ép médiaterem és online
Ideje: 2022 január 24 hétfő d.u. 2 óra



Szívesen látunk minden érdeklődőt.


Balog János







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