2019-07-26 · preprint · Digital Humanities and Scholarship · en
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Given a combinatorial optimisation problem, there are typically multiple ways of modelling it for presentation to an automated solver. Choosing the right combination of model and target solver can have a significant impact on the effectiveness of the solving process. The best combination of model and solver can also be instance-dependent: there may not exist a single combination that works best for all instances of the same problem. We consider the task of building machine learning models to automatically select the best combination for a problem instance. Critical to the learning process is to define instance features, which serve as input to the selection model. Our contribution is the automatic learning of instance features directly from the high-level representation of a problem instance using a transformer encoder. We evaluate the performance of our approach using the Essence modelling language via a case study of three problem classes.
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The work has 16498 citations, a total that marks it as highly cited and already an established reference point in its field, and it references 45 works.
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HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case is a preprint, focusing on Digital Humanities and Scholarship, published in 2019.
HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case has 16,498 citations in our record, placing it among the highly cited works we track.
Our record lists 3 authors for HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case and references 45 other works. Author names aren't in our data yet — the source link on the page lists the full byline.
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