Zobrazeno 1 - 10
of 3 316
pro vyhledávání: '"Döbler AS"'
Autor:
Döbler, Philip, Pflieger, Jannik, Jin, Fengping, De Raedt, Hans, Michielsen, Kristel, Lippert, Thomas, Jattana, Manpreet Singh
In quantum computing, error mitigation is a method to improve the results of an error-prone quantum processor by post-processing them on a classical computer. In this work, we improve the General Error Mitigation (GEM) method for scalability. GEM rel
Externí odkaz:
http://arxiv.org/abs/2411.07916
Prediction Rule Ensembles (PREs) are robust and interpretable statistical learning techniques with potential for predictive analytics, yet their efficacy in the presence of missing data is untested. This study uses multiple imputation to fill in miss
Externí odkaz:
http://arxiv.org/abs/2410.16187
In deep learning, maintaining model robustness against distribution shifts is critical. This work explores a broad range of possibilities to adapt vision-language foundation models at test-time, with a particular emphasis on CLIP and its variants. Th
Externí odkaz:
http://arxiv.org/abs/2405.14977
Autor:
Döbler, Christian
We extend the Malliavin theory for $L^2$-functionals on product probability spaces that has recently been developed by Decreusefond and Halconruy (2019) and by Duerinckx (2021), by characterizing the domains and investigating the actions of the three
Externí odkaz:
http://arxiv.org/abs/2403.14334
This simulation study evaluates the effectiveness of multiple imputation (MI) techniques for multilevel data. It compares the performance of traditional Multiple Imputation by Chained Equations (MICE) with tree-based methods such as Chained Random Fo
Externí odkaz:
http://arxiv.org/abs/2401.14161
Since distribution shifts are likely to occur after a model's deployment and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update the model during test-time, leveraging the current test data. In real
Externí odkaz:
http://arxiv.org/abs/2401.00989
Providing a promising pathway to link the human brain with external devices, Brain-Computer Interfaces (BCIs) have seen notable advancements in decoding capabilities, primarily driven by increasingly sophisticated techniques, especially deep learning
Externí odkaz:
http://arxiv.org/abs/2311.18520
Autor:
Döbler, Christian
We prove a Berry-Esseen bound in de Jong's classical CLT for normalized, completely degenerate $U$-statistics, which says that the convergence of the fourth moment sequence to three and a Lindeberg-Feller type negligibility condition are sufficient f
Externí odkaz:
http://arxiv.org/abs/2307.03189
Since distribution shifts are likely to occur during test-time and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update the model after deployment, leveraging the current test data. Clearly, a method
Externí odkaz:
http://arxiv.org/abs/2306.00650
Since experiencing domain shifts during test-time is inevitable in practice, test-time adaption (TTA) continues to adapt the model after deployment. Recently, the area of continual and gradual test-time adaptation (TTA) emerged. In contrast to standa
Externí odkaz:
http://arxiv.org/abs/2211.13081