Nuclear Hadronic & Few-Body Physics

Name	Category	Phone	Office
Barnea Nir	Faculty	02 6585 138	Popick 114
Bazak Betzalel	Faculty	02 6585 013	Popick 115
Cohen Dan	Graduate Students	02 6584 783	Kaplun Bldg.
Deleon Hilla	Post-Docs		Popick Bldg.
Elyahu Moti	Graduate Students		Popick Bldg.
Friedman Eliahu	Emeriti	02 6584 667	Kaplun 013
Gal Avraham	Emeriti	02 6584 930	Kaplun 209
Gavrielov Noam	Graduate Students	02 6586 788	Kaplun 213
Gazit Doron	Faculty	02 6586 858	Popick 125
Kirscher Johannes	Post-Docs		Ross 1C

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Upcoming Events

Yesterday

Nonlinear Physics seminar: "Statistical mechanics of wide but finite artificial neural networks"
- Start time: 12:00pm
- End date: Wednesday, December 6th
- End time: 01:30pm
- Lecturer: Mr. Gadi Naveh (HUJI)Abstract: Over the past decade, Deep neural networks (DNNs) have brought significant advancements to various fields of science and technology. Key to their success is feature learning, enabling them to extract useful representations from raw data. However, DNNs remain challenging to interpret and lack a comprehensive analytical understanding. Recent studies revealed that DNNs in the infinite width limit are equivalent to Gaussian Processes (GPs), which are well-understoodanalytically but lack feature learning capabilities due to pre-determined features.In this thesis, we aim to bridge the gap between GPs and finite DNNs, using approaches inspired by theoretical physics. Employing statistical field theory, we treat the DNN output as a random object from a statistical ensemble. We performed extensive computer simulations of DNNs trained in both stylized and real-world settings. As for analytical methods, we first employ a perturbative approach to obtain the leading correction in 1/width to the GP limit. Subsequently, we develop a self-consistent mean-field type theory, providing a GP description of finite DNNs with a data-dependent kernel that embodies feature learning properties.
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Position at Kyoto
- Start time: 02:00pm
- End date: Wednesday, December 6th
- End time: 03:00pm
- View this event in Google Calendar

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Nuclear Hadronic & Few-Body Physics

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