Zobrazeno 1 - 10
of 133
pro vyhledávání: '"Kuehl, Niklas"'
Autor:
Spitzer, Philipp, Holstein, Joshua, Morrison, Katelyn, Holstein, Kenneth, Satzger, Gerhard, Kühl, Niklas
Across various applications, humans increasingly use black-box artificial intelligence (AI) systems without insight into these systems' reasoning. To counter this opacity, explainable AI (XAI) methods promise enhanced transparency and interpretabilit
Externí odkaz:
http://arxiv.org/abs/2409.12809
The selection of algorithms is a crucial step in designing AI services for real-world time series classification use cases. Traditional methods such as neural architecture search, automated machine learning, combined algorithm selection, and hyperpar
Externí odkaz:
http://arxiv.org/abs/2409.08636
Federated Learning presents a way to revolutionize AI applications by eliminating the necessity for data sharing. Yet, research has shown that information can still be extracted during training, making additional privacy-preserving measures such as d
Externí odkaz:
http://arxiv.org/abs/2408.08666
Autor:
Kühl, Niklas
Scale-resolving flow simulations often feature several million [thousand] spatial [temporal] discrete degrees of freedom. When storing or re-using these data, e.g., to subsequently train some sort of data-based surrogate or compute consistent adjoint
Externí odkaz:
http://arxiv.org/abs/2407.18093
Artificial intelligence (AI) systems have become an indispensable component of modern technology. However, research on human behavioral responses is lagging behind, i.e., the research into human reliance on AI advice (AI reliance). Current shortcomin
Externí odkaz:
http://arxiv.org/abs/2408.03948
In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffusion models poses numerous challenges, particularly concerning data av
Externí odkaz:
http://arxiv.org/abs/2406.14429
AI is becoming increasingly common across different domains. However, as sophisticated AI-based systems are often black-boxed, rendering the decision-making logic opaque, users find it challenging to comply with their recommendations. Although resear
Externí odkaz:
http://arxiv.org/abs/2406.12660
In numerous high-stakes domains, training novices via conventional learning systems does not suffice. To impart tacit knowledge, experts' hands-on guidance is imperative. However, training novices by experts is costly and time-consuming, increasing t
Externí odkaz:
http://arxiv.org/abs/2406.01329
As organizations face the challenges of processing exponentially growing data volumes, their reliance on analytics to unlock value from this data has intensified. However, the intricacies of big data, such as its extensive feature sets, pose signific
Externí odkaz:
http://arxiv.org/abs/2405.07658
The widespread use of artificial intelligence (AI) systems across various domains is increasingly surfacing issues related to algorithmic fairness, especially in high-stakes scenarios. Thus, critical considerations of how fairness in AI systems might
Externí odkaz:
http://arxiv.org/abs/2404.18736