ORCID Profile
0000-0002-4537-5573
Current Organisation
Trinity College Dublin
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Publisher: ACM
Date: 13-06-2011
Publisher: ACM
Date: 12-10-2013
Publisher: ACM
Date: 13-06-2011
Publisher: ACM
Date: 12-10-2013
Publisher: ACM
Date: 13-06-2010
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer International Publishing
Date: 2016
Publisher: CNRI Acct
Date: 11-2014
Publisher: ACM
Date: 23-05-2018
Publisher: Center for Open Science
Date: 03-09-2020
Abstract: The advances in the field of Automated Machine Learning (AutoML) have greatly reduced human effort in selecting and optimizing machine learning algorithms. These advances, however, have not yet widely made it to Recommender-Systems libraries. We introduce Auto-CaseRec, a Python framework based on the CaseRec recommender-system library. Auto-CaseRec provides automated algorithm selection and parameter tuning for recommendation algorithms. An initial evaluation of Auto-CaseRec against the baselines shows an average 13.88% improvement in RMSE for theMovielens100K dataset and an average 17.95% improvement in RMSE for the Last.fm dataset.
Publisher: University of Toronto Press Inc. (UTPress)
Date: 2010
DOI: 10.3138/JSP.41.2.176
Abstract: This article introduces and discusses the concept of academic search engine optimization (ASEO). Based on three recently conducted studies, guidelines are provided on how to optimize scholarly literature for academic search engines in general, and for Google Scholar in particular. In addition, we briefly discuss the risk of researchers' illegitimately ‘over-optimizing’ their articles.
Publisher: Springer International Publishing
Date: 2018
Publisher: Center for Open Science
Date: 21-04-2020
Abstract: The relatedness of research articles, patents, court rulings, webpages, and other document types is often calculated with citation or hyperlink-based approaches like co-citation (proximity) analysis. The main limitation of citation-based approaches is that they cannot be used for documents that receive little or no citations. We propose Virtual Citation Proximity (VCP), a Siamese Neural Network architecture, which combines the advantages of co-citation proximity analysis ( erse notions of relatedness / high recommendation performance), with the advantage of content-based filtering (high coverage). VCP is trained on a corpus of documents with textual features, and with real citation proximity as ground truth. VCP then predicts for any two documents, based on their title and abstract, in what proximity the two documents would be co-cited, if they were indeed co-cited. The prediction can be used in the same way as real citation proximity to calculate document relatedness, even for uncited documents. In our evaluation with 2 million co-citations from Wikipedia articles, VCP achieves an MAE of 0.0055, i.e. an improvement of 20% over the baseline, though the learning curve suggests that more work is needed.
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 03-2016
Publisher: ACM
Date: 19-09-2011
Publisher: IEEE
Date: 2009
Publisher: ACM
Date: 22-07-2013
Publisher: ACM
Date: 22-07-2013
Publisher: ACM
Date: 22-07-2013
Publisher: Center for Open Science
Date: 28-01-2021
Abstract: Neural Architecture Search research has been limited to fixed datasets and as such does not provide the flexibility needed to deal with real-world, constantly evolving data. This is why we propose the basis of Online Neural Architecture Search (ONAS) to deal with complex, evolving, data distributions. We formalise ONAS as a minimisation problem upon which both the weights and the architecture of the neural network needs to be optimised for the data up until a time $t_i$. To solve this problem, we adapt a DARTS optimisation process, associated with an early stopping scheme, by using the supernet optimised on previous data as a warm-up initial state. This allows the architecture of the neural network to evolve as the data distribution evolves while limiting the computational burden. This work aims at building the initial mathematical formalism of the problem as well as the development of a framework where NAS methods could be used to solve this problem. Finally, several possible next steps are presented to show the potential of this field of Online Neural Architecture Search.
Publisher: ACM
Date: 13-06-2010
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 26-07-2015
Publisher: ACM
Date: 14-09-2023
Publisher: IEEE
Date: 04-2009
Publisher: University of Michigan Library
Date: 12-2010
Publisher: Springer International Publishing
Date: 2015
Location: United Kingdom of Great Britain and Northern Ireland
Location: United States of America
Location: Australia
No related grants have been discovered for Joeran Beel.