15 citations · 49 across the 12 of their papers we have counts for
12 papers
Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering
Andrew Collins, Laura Tierney, Joeran Beel
Recommendation algorithms perform differently if the users, recommendation contexts, applications, and user interfaces vary even slightly. It is similarly observed in other fields,…
Finite Group Equivariant Neural Networks for Games
Oisín Carroll, Joeran Beel
Games such as go, chess and checkers have multiple equivalent game states, i.e. multiple board positions where symmetrical and opposite moves should be made. These equivalences are…
Towards an Interoperable Data Protocol Aimed at Linking the Fashion Industry with AI Companies
Mohammed Al-Rawi, Joeran Beel
The fashion industry is looking forward to use artificial intelligence technologies to enhance their processes, services, and applications. Although the amount of fashion data curr…
Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization
Rohan Anand, Joeran Beel
We introduce Auto-Surprise, an Automated Recommender System library. Auto-Surprise is an extension of the Surprise recommender system library and eases the algorithm selection and…
Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]
Joeran Beel, Bryan Tyrell, Edward Bergman +2
Automated per-instance algorithm selection often outperforms single learners. Key to algorithm selection via meta-learning is often the (meta) features, which sometimes though do n…
Synthetic vs. Real Reference Strings for Citation Parsing, and the Importance of Re-training and Out-Of-Sample Data for Meaningful Evaluations: Experiments with GROBID, GIANT and Cora
Mark Grennan, Joeran Beel
Citation parsing, particularly with deep neural networks, suffers from a lack of training data as available datasets typically contain only a few thousand training instances. Manua…