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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]


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Scaling Guarantees for Nearest Counterfactual Explanations

Mohammadi, K., Karimi, A., Barthe, G., Valera, I.

2020 (misc) Submitted

ei

arXiv [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]


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Nanoscale Materials for Energy Storage
{Materials Science \& Engineering B}, 108, pages: 292, Elsevier, 2004 (misc)

mms

[BibTex]

[BibTex]