Titlebook: Artificial Intelligence and Machine Learning; 34th Joint Benelux C Toon Calders,Celine Vens,Bart Goethals Conference proceedings 2023 The E

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,Symmetry and Dominance Breaking for Pseudo-Boolean Optimization,s problem is to introduce so-called symmetry breaking constraints, which eliminate some symmetric parts of the search space. In this paper, we focus on ., which are specified by a set of 0–1 integer linear inequalities (also known as .) and a linear objective. Symmetry breaking has already been stud

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https://doi.org/10.1007/978-3-031-52846-0study. If automatic misinformation detection is applied in a real-world setting, it is necessary to validate the methods being used. Large language models (LLMs) have produced the best results among text-based methods. However, fine-tuning such a model requires a significant amount of training data,

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https://doi.org/10.1007/978-981-97-2393-5arners, not all learning curve behavior is well understood. For instance, it is sometimes assumed that the more training data provided, the better the learner performs. However, counter-examples exist for both classical machine learning algorithms and deep neural networks, where errors do not monoto

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Boundary Stabilization of the Wave Equation,proving customer satisfaction. A core way they achieve this is by providing customers with an easy access to their products by structuring them in catalogues using navigation bars and providing recommendations. We refer to these catalogues as ., e.g. product categories on e-commerce websites, public

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