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pro vyhledávání: '"Sebastian, G"'
Autor:
Gruber, Sebastian G., Bach, Francis
In this work, we propose a mean-squared error-based risk that enables the comparison and optimization of estimators of squared calibration errors in practical settings. Improving the calibration of classifiers is crucial for enhancing the trustworthi
Externí odkaz:
http://arxiv.org/abs/2410.07014
The evaluation of image generators remains a challenge due to the limitations of traditional metrics in providing nuanced insights into specific image regions. This is a critical problem as not all regions of an image may be learned with similar ease
Externí odkaz:
http://arxiv.org/abs/2409.01314
Large language models (LLMs) are deep learning algorithms being used to perform natural language processing tasks in various fields, from social sciences to finance and biomedical sciences. Developing and training a new LLM can be very computationall
Externí odkaz:
http://arxiv.org/abs/2401.11011
Autor:
Popordanoska, Teodora, Gruber, Sebastian G., Tiulpin, Aleksei, Buettner, Florian, Blaschko, Matthew B.
Proper scoring rules evaluate the quality of probabilistic predictions, playing an essential role in the pursuit of accurate and well-calibrated models. Every proper score decomposes into two fundamental components -- proper calibration error and ref
Externí odkaz:
http://arxiv.org/abs/2312.08589
Generative models, like large language models, are becoming increasingly relevant in our daily lives, yet a theoretical framework to assess their generalization behavior and uncertainty does not exist. Particularly, the problem of uncertainty estimat
Externí odkaz:
http://arxiv.org/abs/2310.05833
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-11 (2024)
Abstract Crustal thickening associated with orogenic growth elevates topography, causing orographic enhancement of precipitation, which in turn facilitates local erosion and possibly intensifies localization of deformation. How these three processes
Externí odkaz:
https://doaj.org/article/38877dcdfbab4a95b126d023dff7bc3c