People by Discipline

Name	Discipline	Category	Phone	Office
Davidov Dan	Condensed Matter Physics	Emeriti	02 6584 637	Danciger B 106
Dekel Avishai	Astrophysics and Relativity	Faculty	02 6584 100	Kaplun 107
Deleon Hilla	Nuclear Hadronic and Few-Body Physics	Post-Docs		Popick Bldg.
Dimanstein Noya	Condensed Matter Physics	Graduate Students		Popick Bldg.
Dvir Tom	Condensed Matter Physics	Graduate Students	02 6594 031	Kaplun Bldg.
Eisenberg Hagai	Atomic, Molecular and Optical Physics	Faculty	02 6585 228	Marks
Elitzur Shmuel	High Energy Physics	Emeriti	02 6585 672	Kaplun 203
Elyahu Moti	Nuclear Hadronic and Few-Body Physics	Graduate Students		Popick Bldg.
Engelberg Eli	Condensed Matter Physics	Graduate Students		Popick Bldg.
Faerman Yakov	Astrophysics and Relativity	Post-Docs

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
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