People by Discipline

Name	Discipline	Category	Phone	Office
Smolkin Michael	High Energy Physics	Faculty	02 6584 374	Kaplun 106
Kol Barak	High Energy Physics	Faculty	02 6584 929	Kaplun 210
Kramer Eric David	High Energy Physics	Post-Docs		Ross -2 1D
Haskins Laurel Stephenson	High Energy Physics	Post-Docs		Ross -2 1D
Mazumdar Subhajit	High Energy Physics	Post-Docs	02 6584 890	Kaplun Bldg. 212
Elitzur Shmuel	High Energy Physics	Emeriti	02 6585 672	Kaplun 203
Giveon Amit	High Energy Physics	Faculty	02 6584 646	Kaplun 207
Shubho Roy	High Energy Physics	Post-Docs	02 6584 354	Kaplun 202
Ren Jie	High Energy Physics	Post-Docs	02 6584 890	Kaplun 212
Shir Ruth	High Energy Physics	Post-Docs	02 6584 173	Kaplun 214

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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