156 citations · 950 across the 61 of their papers we have counts for
6 papers · 1 filter
Towards an Integrative Educational Recommender for Lifelong Learners
Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz +1
One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities betwee…
TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources
Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz +1
The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality educa…
Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation
Gaurav Singh, Zahra Sabet, John Shawe-Taylor +1
Biomedical text tagging systems are plagued by the dearth of labeled training data. There have been recent attempts at using pre-trained encoders to deal with this issue. Pre-train…
Data-Driven Malaria Prevalence Prediction in Large Densely-Populated Urban Holoendemic sub-Saharan West Africa: Harnessing Machine Learning Approaches and 22-years of Prospectively Collected Data
Biobele J. Brown, Alexander A. Przybylski, Petru Manescu +20
Plasmodium falciparum malaria still poses one of the greatest threats to human life with over 200 million cases globally leading to half-million deaths annually. Of these, 90% of c…
Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy
Petru Manescu, Lydia Neary- Zajiczek, Michael J. Shaw +10
Fast accurate diagnosis of malaria is still a global health challenge for which automated digital-pathology approaches could provide scalable solutions amenable to be deployed in l…
Model Validation Using Mutated Training Labels: An Exploratory Study
Jie M. Zhang, Mark Harman, Benjamin Guedj +2
We introduce an exploratory study on Mutation Validation (MV), a model validation method using mutated training labels for supervised learning. MV mutates training data labels, ret…