John Quinn
John Quinn
Google Research, Sunbird AI, Makerere University
Verified email at - Homepage
Cited by
Cited by
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
Continental-scale building detection from high resolution satellite imagery
W Sirko, S Kashubin, M Ritter, A Annkah, YSE Bouchareb, Y Dauphin, ...
arXiv preprint arXiv:2107.12283, 2021
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
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
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
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
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
Known unknowns: Novelty detection in condition monitoring
JA Quinn, CKI Williams
Iberian Conference on Pattern Recognition and Image Analysis, 1-6, 2007
A least-squares approach to anomaly detection in static and sequential data
JA Quinn, M Sugiyama
Pattern Recognition Letters 40, 36-40, 2014
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
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
Location Segmentation, Inference and Prediction for Anticipatory Computing.
N Eagle, A Clauset, JA Quinn
AAAI Spring Symposium: Technosocial Predictive Analytics, 20-25, 2009
Automated Vision-Based Diagnosis of Cassava Mosaic Disease.
JR Aduwo, E Mwebaze, JA Quinn
ICDM (Workshops), 114-122, 2010
Computational sustainability and artificial intelligence in the developing world
J Quinn, V Frias-Martinez, L Subramanian
AI Magazine 35 (3), 36-47, 2014
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
Modeling and monitoring crop disease in developing countries
J Quinn, K Leyton-Brown, E Mwebaze
Proceedings of the AAAI Conference on Artificial Intelligence 25 (1), 1390-1395, 2011
Methodologies for continuous cellular tower data analysis
N Eagle, JA Quinn, A Clauset
Pervasive Computing: 7th International Conference, Pervasive 2009, Nara …, 2009
Identifying seasonal mobility profiles from anonymized and aggregated mobile phone data. Application in food security
PJ Zufiria, D Pastor-Escuredo, L Úbeda-Medina, MA Hernandez-Medina, ...
PloS one 13 (4), e0195714, 2018
Feature exploration for almost zero-resource ASR-free keyword spotting using a multilingual bottleneck extractor and correspondence autoencoders
R Menon, H Kamper, E Van Der Westhuizen, J Quinn, T Niesler
arXiv preprint arXiv:1811.08284, 2018
Fast ASR-free and almost zero-resource keyword spotting using DTW and CNNs for humanitarian monitoring
R Menon, H Kamper, J Quinn, T Niesler
arXiv preprint arXiv:1806.09374, 2018
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