195 citations · 203 across the 9 of their papers we have counts for
10 papers
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…
PySINDy: A comprehensive Python package for robust sparse system identification
Alan A. Kaptanoglu, Brian M. de Silva, Urban Fasel +9
Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySIN…
Predicting United States policy outcomes with Random Forests
Shawn McGuire, Charles Delahunt
Two decades of U.S. government legislative outcomes, as well as the policy preferences of rich people, the general population, and diverse interest groups, were captured in a detai…
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…
Insect cyborgs: Bio-mimetic feature generators improve machine learning accuracy on limited data
Charles B Delahunt, J Nathan Kutz
Machine learning (ML) classifiers always benefit from more informative input features. We seek to auto-generate stronger feature sets in order to address the difficulty that ML met…
Built to Last: Functional and structural mechanisms in the moth olfactory network mitigate effects of neural injury
Charles B Delahunt, Pedro D Maia, J. Nathan Kutz
Most organisms suffer neuronal damage throughout their lives, which can impair performance of core behaviors. Their neural circuits need to maintain function despite injury, which…
Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural Nets
Charles B. Delahunt, Jeffrey A. Riffell, J. Nathan Kutz
The insect olfactory system, which includes the antennal lobe (AL), mushroom body (MB), and ancillary structures, is a relatively simple neural system capable of learning. Its stru…
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…