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A machine learning route between band mapping and band structure
A machine learning route between band mapping and band structure

Xian*, R. P., Stimper*, V., Zacharias, M., Dong, S., Dendzik, M., Beaulieu, S., Schölkopf, B., Wolf, M., Rettig, L., Carbogno, C., Bauer, S., Ernstorfer, R.

2020, *equal contribution (misc)

ei

arXiv [BibTex]

arXiv [BibTex]

2017


Crowdshaping Realistic {3D} Avatars with Words
Crowdshaping Realistic 3D Avatars with Words

Streuber, S., Ramirez, M. Q., Black, M., Zuffi, S., O’Toole, A., Hill, M. Q., Hahn, C. A.

August 2017, Application PCT/EP2017/051954 (misc)

Abstract
A method for generating a body shape, comprising the steps: - receiving one or more linguistic descriptors related to the body shape; - retrieving an association between the one or more linguistic descriptors and a body shape; and - generating the body shape, based on the association.

ps

Google Patents [BibTex]

2017


Google Patents [BibTex]


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Method and device for recovering a digital image from a sequence of observed digital images

Harmeling, S., Hirsch, M., Sra, S., Schölkopf, B.

United States Provisional Patent Application, No 61387025, September 2010 (patent)

ei

[BibTex]

[BibTex]

2004


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Statistische Lerntheorie und Empirische Inferenz

Schölkopf, B.

Jahrbuch der Max-Planck-Gesellschaft, 2004, pages: 377-382, 2004 (misc)

Abstract
Statistical learning theory studies the process of inferring regularities from empirical data. The fundamental problem is what is called generalization: how it is possible to infer a law which will be valid for an infinite number of future observations, given only a finite amount of data? This problem hinges upon fundamental issues of statistics and science in general, such as the problems of complexity of explanations, a priori knowledge, and representation of data.

ei

PDF Web [BibTex]

2004


PDF Web [BibTex]