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07/17/2023
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML
Automated machine learning (AutoML) systems commonly ensemble models pos...
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07/01/2023
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure
Many state-of-the-art automated machine learning (AutoML) systems use gr...
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07/01/2023
Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML
Automated Machine Learning (AutoML) frameworks regularly use ensembles. ...
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12/30/2020
Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering
Recommendation algorithms perform differently if the users, recommendati...
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09/10/2020
Finite Group Equivariant Neural Networks for Games
Games such as go, chess and checkers have multiple equivalent game state...
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09/07/2020
Towards an Interoperable Data Protocol Aimed at Linking the Fashion Industry with AI Companies
The fashion industry is looking forward to use artificial intelligence t...
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08/19/2020
Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization
We introduce Auto-Surprise, an Automated Recommender System library. Aut...
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06/22/2020
Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]
Automated per-instance algorithm selection often outperforms single lear...
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04/22/2020
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 a
Citation parsing, particularly with deep neural networks, suffers from a...
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12/18/2019
Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems
The effectiveness of recommender system algorithms varies in different r...
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12/16/2019
Predicting the Outcome of Judicial Decisions made by the European Court of Human Rights
In this study, machine learning models were constructed to predict wheth...
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12/16/2019
Multi-stream Data Analytics for Enhanced Performance Prediction in Fantasy Football
Fantasy Premier League (FPL) performance predictors tend to base their a...
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12/15/2019
NaïveRole: Author-Contribution Extraction and Parsing from Biomedical Manuscripts
Information about the contributions of individual authors to scientific ...
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09/18/2019
Memory-Augmented Neural Networks for Machine Translation
Memory-augmented neural networks (MANNs) have been shown to outperform o...
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05/27/2019
Document Embeddings vs. Keyphrases vs. Terms: An Online Evaluation in Digital Library Recommender Systems
Many recommendation algorithms are available to digital library recommen...
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11/26/2018
ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers
Bibliographic reference parsers extract machine-readable metadata such a...
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11/26/2018
The Architecture of Mr. DLib's Scientific Recommender-System API
Recommender systems in academia are not widely available. This may be in...
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09/27/2018
An Empirical Comparison of Syllabuses for Curriculum Learning
Syllabuses for curriculum learning have been developed on an ad-hoc, per...
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08/27/2018
ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing
Bibliographic reference parsers extract metadata (e.g. author names, tit...
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07/23/2018
Implementing Neural Turing Machines
Neural Turing Machines (NTMs) are an instance of Memory Augmented Neural...
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07/19/2018
Mr. DLib's Living Lab for Scholarly Recommendations
We introduce the first living lab for scholarly recommender systems. Thi...
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07/18/2018
RARD II: The 2nd Related-Article Recommendation Dataset
The main contribution of this paper is to introduce and describe a new r...
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05/30/2018
One-at-a-time: A Meta-Learning Recommender-System for Recommendation-Algorithm Selection on Micro Level
In this proposal we present the idea of a "macro recommender system", an...
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05/12/2018
Citation Data-set for Machine Learning Citation Styles and Entity Extraction from Citation Strings
Citation parsing is fundamental for search engines within academia and t...
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02/19/2018
A Study of Position Bias in Digital Library Recommender Systems
"Position bias" describes the tendency of users to interact with items o...
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02/04/2018
A Method for Discovering and Extracting Author Contributions Information from Scientific Biomedical Publications
Creating scientific publications is a complex process, typically compose...
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02/04/2018
Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers
Bibliographic reference parsing refers to extracting machine-readable me...
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02/04/2018
Evaluation and Comparison of Open Source Bibliographic Reference Parsers: A Business Use Case
Bibliographic reference parsing refers to extracting machine-readable me...
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08/28/2017
It's Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal]
In this position paper, we question the current practice of calculating ...
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03/27/2017
