195 citations · 203 across the 9 of their papers we have counts for
5 papers · 1 filter
Metrics to guide development of machine learning algorithms for malaria diagnosis
Charles B. Delahunt, Noni Gachuhi, Matthew P. Horning
Automated malaria diagnosis is a difficult but high-value target for machine learning (ML), and effective algorithms could save many thousands of children's lives. However, current…
A toolkit for data-driven discovery of governing equations in high-noise regimes
Charles B. Delahunt, J. Nathan Kutz
We consider the data-driven discovery of governing equations from time-series data in the limit of high noise. The algorithms developed describe an extensive toolkit of methods for…
Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information
Charles B. Delahunt, Mayoore S. Jaiswal, Matthew P. Horning +22
Malaria is a life-threatening disease affecting millions. Microscopy-based assessment of thin blood films is a standard method to (i) determine malaria species and (ii) quantitate…
Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy
Charles B. Delahunt, Courosh Mehanian, J. Nathan Kutz
Softmax is a standard final layer used in Neural Nets (NNs) to summarize information encoded in the trained NN and return a prediction. However, Softmax leverages only a subset of…
Putting a bug in ML: The moth olfactory network learns to read MNIST
Charles B. Delahunt, J. Nathan Kutz
We seek to (i) characterize the learning architectures exploited in biological neural networks for training on very few samples, and (ii) port these algorithmic structures to a mac…