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Benelearn 2018 accepted papers and abstracts

Frans Oliehoek, Rahul Savani, Jose Gallego, Elise van der Pol and Roderich Gross. Beyond Local Nash Equilibria for Adversarial Networks

Victor Hamer and Pierre Dupont. Learning Computationally Efficient Metrics for Large Scale Person Identification

Frederique van den Boogaart, Thomas Hantke and Martijn van Otterlo. Predicting next week redemption in a digital frequency loyalty program

Francesco Dal Canton, Vincent Quinten and Marco Wiering. Early Detection of Sepsis Induced Deterioration Using Machine Learning

Joris Roels, Julian Hennies, Wilfried Philips, Yvan Saeys and Anna Kreshuk. Towards high throughput volume electron microscopy segmentation through deep domain adaptation

Antonio Pereira Barata, Gerrit Jan de Bruin, Frank Takes, Jaap van den Herik and Cor Veenman. Finding Anomalies in Waste Transportation Data with Supervised Category Models

Daniel Peralta, Isaac Triguero Velázquez, Salvador García, Yvan Saeys, José M. Benítez and Francisco Herrera. On the use of convolutional neural networks for robust classification of multiple fingerprint captures

Lotte Bransen, Michel van de Velden and Jan Van Haaren. Valuing passes in football using ball event data

Merel Jung, Bram van den Berg, Eric Postma and Willem Huijbers. Inferring PET from MRI with pix2pix

Rémi Delanghe, Pieterjan Criel and Tom Dhaene. Periodic Reranking Strategy in Unsupervised Recommender Systems

Maarten van Hees, Peter van der Putten and Maarten H Lamers. Disciples of the Heinous Path: Exploring Label Structure in Heavy Metal Genres

Nazly Santos Buitrago, Loek Tonnaer, Vlado Menkovski and Dimitrios Mavroeidis. Anomaly Detection for imbalanced datasets with Deep Generative Models

Jose Velasquez Sosa and Gerasimos Spanakis. Neural Attention and Morphological Word Embedding for Contract Element Extraction

Ákos Kádár, Desmond Elliott, Marc-Alexandre Côté, Grzegorz Chrupała and Afra Alishahi. Lessons learned in multilingual grounded language learning

Evgeniya Korneva and Hendrik Blockeel. Model Selection for Multi-Directional Ensemble of Regression and Classification Trees