17 citations · 88 across the 17 of their papers we have counts for
18 papers
ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers
Dominika Tkaczyk, Rohit Gupta, Riccardo Cinti +1
Bibliographic reference parsers extract machine-readable metadata such as author names, title, journal, and year from bibliographic reference strings. To extract the metadata, the…
The Architecture of Mr. DLib's Scientific Recommender-System API
Joeran Beel, Andrew Collins, Akiko Aizawa
Recommender systems in academia are not widely available. This may be in part due to the difficulty and cost of developing and maintaining recommender systems. Many operators of ac…
An Empirical Comparison of Syllabuses for Curriculum Learning
Mark Collier, Joeran Beel
Syllabuses for curriculum learning have been developed on an ad-hoc, per task basis and little is known about the relative performance of different syllabuses. We identify a number…
ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing
Dominika Tkaczyk, Paraic Sheridan, Joeran Beel
Bibliographic reference parsers extract metadata (e.g. author names, title, year) from bibliographic reference strings. No reference parser consistently gives the best results in e…
Implementing Neural Turing Machines
Mark Collier, Joeran Beel
Neural Turing Machines (NTMs) are an instance of Memory Augmented Neural Networks, a new class of recurrent neural networks which decouple computation from memory by introducing an…
Online Evaluations for Everyone: Mr. DLib's Living Lab for Scholarly Recommendations
Joeran Beel, Andrew Collins, Oliver Kopp +2
We introduce the first 'living lab' for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for sc…