6 papers
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…
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…