Title:
Lecture 5: Mathematics for Deep Neural Networks: Energy landscape and open problems
Lecture 5: Mathematics for Deep Neural Networks: Energy landscape and open problems
| dc.contributor.author | Schmidt-Hieber, Johannes | |
| dc.contributor.corporatename | Georgia Institute of Technology. Transdisciplinary Research Institute for Advancing Data Science | en_US |
| dc.contributor.corporatename | University of Twente. Dept. of Applied Mathematics | en_US |
| dc.date.accessioned | 2019-03-28T18:11:45Z | |
| dc.date.available | 2019-03-28T18:11:45Z | |
| dc.date.issued | 2019-03-18 | |
| dc.description | Presented on March 18, 2019 at 10:30 a.m. in the Groseclose Building, Room 402. | en_US |
| dc.description | Johannes Schmidt-Hieber is the Chair of Statistics in the Department of Applied Mathematics at the University of Twente. His research topics include statistical theory for deep neural networks, nonparametric Bayes, confidence statements for qualitative constraints, asymptotic equivalence, and spot volatility estimation. | en_US |
| dc.description | Runtime: 62:40 minutes | en_US |
| dc.description.abstract | To derive a theory for gradient descent methods, it is important to have some understanding of the energy landscape. In this lecture, an overview of existing results is given. The second part of the lecture is devoted to future challenges in the field. We describe important future steps needed for the future development of the statistical theory of deep networks. | en_US |
| dc.format.extent | 62:40 minutes | |
| dc.identifier.uri | http://hdl.handle.net/1853/60958 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | TRIAD Distinguished Lecture Series | en_US |
| dc.subject | Deep neural networks | en_US |
| dc.subject | Kolmogorov-Arnold | en_US |
| dc.subject | Localization | en_US |
| dc.title | Lecture 5: Mathematics for Deep Neural Networks: Energy landscape and open problems | en_US |
| dc.type | Moving Image | |
| dc.type.genre | Lecture | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Transdisciplinary Research Institute for Advancing Data Science | |
| local.relation.ispartofseries | Transdisciplinary Research Institute for Advancing Data Science Lectures | |
| relation.isOrgUnitOfPublication | 09be376c-3b5f-4fa8-9e58-6a3595a8353b | |
| relation.isSeriesOfPublication | f402db73-162f-4a58-a9d2-bc56b6a0af52 |
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