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Titlebook: KI 2023: Advances in Artificial Intelligence; 46th German Conferen Dietmar Seipel,Alexander Steen Conference proceedings 2023 The Editor(s)

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31#
發(fā)表于 2025-3-26 23:56:02 | 只看該作者
32#
發(fā)表于 2025-3-27 01:56:23 | 只看該作者
33#
發(fā)表于 2025-3-27 06:30:00 | 只看該作者
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/k/image/541652.jpg
34#
發(fā)表于 2025-3-27 11:30:33 | 只看該作者
,Optimisation of?Matrix Production System Reconfiguration with?Reinforcement Learning,f an MPS. We define two operational scenarios and evaluate the potential of RL in each. Taken more generally, our results provide context to better understand when RL can be beneficial in industrial-scale use cases.
35#
發(fā)表于 2025-3-27 17:00:51 | 只看該作者
: ,emistry ,ware ,onvex ,ull ,utoencoder Towards Inverse Molecular Design,resentation for inverse design to discover novel molecules. However, exploring this latent space requires particular insights to generate new structures. Therefore, we propose using a convex hull (CH) surrounding the top molecules regarding high QEDs to ensnare a tight subspace in the latent represe
36#
發(fā)表于 2025-3-27 20:12:01 | 只看該作者
37#
發(fā)表于 2025-3-27 23:04:42 | 只看該作者
,Interactive Link Prediction as?a?Downstream Task for?Foundational GUI Understanding Models, a second screen (target screen). If this task is solved sufficiently, it can demonstrate an understanding of the relationship between elements and components across screens and enable various applications in GUI design automation and assistance. To encourage and support research on interactive link
38#
發(fā)表于 2025-3-28 02:51:09 | 只看該作者
Harmonizing Feature Attributions Across Deep Learning Architectures: Enhancing Interpretability andoss diverse deep-learning models. Our findings highlight the potential for harmonized feature attribution methods to improve interpretability and foster trust in machine learning applications, regardless of the underlying architecture.
39#
發(fā)表于 2025-3-28 06:35:05 | 只看該作者
,Detecting Floors in?Residential Buildings,nd existing approaches are completely black-box so that it is difficult to determine when and why the prediction is wrong..In this paper we propose a grey-box approach. In a stepwise process we can predict floor counts with high quality and remain explainable and parametrizable. By using data that i
40#
發(fā)表于 2025-3-28 12:02:49 | 只看該作者
,A Comparative Study of?Video-Based Analysis Using Machine Learning for?Polyp Classification,e integration of such a voting system within the broader framework of video-based polyp identification and provides an empirical evaluation of its performance when juxtaposed with contemporary models. The findings underscore the potential of the proposed system in advancing colorectal polyp classifi
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