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pro vyhledávání: '"Becker Alexander"'
Autor:
Becker, Alexander, Wegner, Jan D., Dawoe, Evans, Schindler, Konrad, Thompson, William J., Bunn, Christian, Garrett, Rachael D., Castro, Fabio, Hart, Simon P., Blaser-Hart, Wilma J.
Reconciling agricultural production with climate-change mitigation and adaptation is one of the most formidable problems in sustainability. One proposed strategy for addressing this problem is the judicious retention of trees in agricultural systems.
Externí odkaz:
http://arxiv.org/abs/2410.20882
Autor:
Halbheer, Michelle, Mühlematter, Dominik J., Becker, Alexander, Narnhofer, Dominik, Aasen, Helge, Schindler, Konrad, Turkoglu, Mehmet Ozgur
Numerous crucial tasks in real-world decision-making rely on machine learning algorithms with calibrated uncertainty estimates. However, modern methods often yield overconfident and uncalibrated predictions. Various approaches involve training an ens
Externí odkaz:
http://arxiv.org/abs/2405.14438
Recent approaches for arbitrary-scale single image super-resolution (ASSR) have used local neural fields to represent continuous signals that can be sampled at arbitrary rates. However, the point-wise query of the neural field does not naturally matc
Externí odkaz:
http://arxiv.org/abs/2311.17643
Autor:
de Becker, Alexander, Head, Linus, Bonfond, Bertrand, Jehin, Emmanuël, Manfroid, Jean, Yao, Zhonghua, Zhang, Binzheng, Grodent, Denis, Schneider, Nicholas, Benkhaldoun, Zouhair
Publikováno v:
A&A 680, A3 (2023)
Io is the most volcanically active body in the Solar System. This volcanic activity results in the ejection of material into Io's atmosphere, which may then escape from the atmosphere to form various structures in the jovian magnetosphere, including
Externí odkaz:
http://arxiv.org/abs/2309.04371
Autor:
Becker, Alexander De, author, Claus, Flore, author
Publikováno v:
Labour Law Utopias : Post-Growth & Post-Productive Work Approaches, 2024.
Externí odkaz:
https://doi.org/10.1093/oso/9780198889755.003.0011
Autor:
Becker, Alexander, Liebig, Thomas
Data protection regulations like the GDPR or the California Consumer Privacy Act give users more control over the data that is collected about them. Deleting the collected data is often insufficient to guarantee data privacy since it is often used to
Externí odkaz:
http://arxiv.org/abs/2210.01451
Autor:
Becker, Alexander, Liebig, Thomas
There has been a growing interest in Machine Unlearning recently, primarily due to legal requirements such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act. Thus, multiple approaches were presented to remove th
Externí odkaz:
http://arxiv.org/abs/2208.10836
Autor:
Turkoglu, Mehmet Ozgur, Becker, Alexander, Gündüz, Hüseyin Anil, Rezaei, Mina, Bischl, Bernd, Daudt, Rodrigo Caye, D'Aronco, Stefano, Wegner, Jan Dirk, Schindler, Konrad
The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncertainty predictions. A common approach to quantify epistemic uncertaint
Externí odkaz:
http://arxiv.org/abs/2206.00050
Autor:
de Lutio, Riccardo, Becker, Alexander, D'Aronco, Stefano, Russo, Stefania, Wegner, Jan D., Schindler, Konrad
We introduce a novel formulation for guided super-resolution. Its core is a differentiable optimisation layer that operates on a learned affinity graph. The learned graph potentials make it possible to leverage rich contextual information from the gu
Externí odkaz:
http://arxiv.org/abs/2203.14297