John Quinn
John Quinn
Google AI Ghana, Makerere University
Verified email at - Homepage
Cited by
Cited by
Factorial switching linear dynamical systems applied to physiological condition monitoring
JA Quinn, CKI Williams, N McIntosh
IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (9), 1537-1551, 2008
A comparison of graphical and textual presentations of time series data to support medical decision making in the neonatal intensive care unit
AS Law, Y Freer, J Hunter, RH Logie, N Mcintosh, J Quinn
Journal of Clinical Monitoring and Computing 19 (3), 183-194, 2005
Divergence-based classification in learning vector quantization
E Mwebaze, P Schneider, FM Schleif, JR Aduwo, JA Quinn, S Haase, ...
Neurocomputing 74 (9), 1429-1435, 2011
Deep convolutional neural networks for microscopy-based point of care diagnostics
JA Quinn, R Nakasi, PKB Mugagga, P Byanyima, W Lubega, A Andama
Machine Learning for Healthcare Conference, 271-281, 2016
Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care
C Williams, J Quinn, N McIntosh
Advances in Neural Information Processing Systems 18, 2006
A least-squares approach to anomaly detection in static and sequential data
JA Quinn, M Sugiyama
Pattern Recognition Letters 40, 36-40, 2014
Known unknowns: Novelty detection in condition monitoring
JA Quinn, CKI Williams
Iberian Conference on Pattern Recognition and Image Analysis, 1-6, 2007
Location Segmentation, Inference and Prediction for Anticipatory Computing.
N Eagle, A Clauset, JA Quinn
AAAI Spring Symposium: Technosocial Predictive Analytics, 20-25, 2009
Methodologies for continuous cellular tower data analysis
N Eagle, JA Quinn, A Clauset
International Conference on Pervasive Computing, 342-353, 2009
Automated Blood Smear Analysis for Mobile Malaria Diagnosis
JA Quinn, A Andama, I Munabi, FN Kiwanuka
Mobile Point-of-Care Monitors and Diagnostic Device Design, 2014
Modeling and monitoring crop disease in developing countries
JA Quinn, K Leyton-Brown, E Mwebaze
Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011
Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping
JA Quinn, MM Nyhan, C Navarro, D Coluccia, L Bromley, M Luengo-Oroz
Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2018
Direct learning of sparse changes in Markov networks by density ratio estimation
S Liu, JA Quinn, MU Gutmann, T Suzuki, M Sugiyama
Neural computation 26 (6), 1169-1197, 2014
Automated Vision-Based Diagnosis of Cassava Mosaic Disease.
JR Aduwo, E Mwebaze, JA Quinn
Industrial Conference on Data Mining-Workshops, 114-122, 2010
Traffic flow monitoring in crowded cities
JA Quinn, R Nakibuule
2010 AAAI Spring Symposium Series, 2010
Computational sustainability and artificial intelligence in the developing world
J Quinn, V Frias-Martinez, L Subramanian
AI Magazine 35 (3), 36-47, 2014
Bayesian Condition Monitoring in Neonatal Intensive Care
J Quinn
University of Edinburgh, 2007
Automated Vision-Based Diagnosis of Banana Bacterial Wilt Disease and Black Sigatoka Disease
G Owomugisha, JA Quinn, E Mwebaze, J Lwasa
The 1st International Conference on the Use of Mobile Information and …, 2014
Very Low Resource Radio Browsing for Agile Developmental and Humanitarian Monitoring.
A Saeb, R Menon, H Cameron, W Kibira, J Quinn, T Niesler
INTERSPEECH, 2118-2122, 2017
A mobile market for agricultural trade in Uganda
R Ssekibuule, JA Quinn, K Leyton-Brown
Proceedings of the 4th Annual Symposium on Computing for Development, 1-10, 2013
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