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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
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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
- [Fizinfo] Wigner FK RMI Elméleti Osztály Szemináriuma, Balog Janos, 01/18/2022
- [Fizinfo] Wigner FK RMI Elméleti Osztály Szemináriuma, Balog Janos, 01/22/2022
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