19 citations · 19 across the 4 of their papers we have counts for
4 papers
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence
Erik C. Johnson, Brian S. Robinson, Gautam K. Vallabha +17
Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense a…
Utilizing a null class to restrict decision spaces and defend against neural network adversarial attacks
Matthew J. Roos
Despite recent progress, deep neural networks generally continue to be vulnerable to so-called adversarial examples--input images with small perturbations that can result in change…
Neural Reconstruction Integrity: A metric for assessing the connectivity of reconstructed neural networks
Elizabeth P. Reilly, Jeffrey S. Garretson, William Gray Roncal +4
Neuroscientists are actively pursuing high-precision maps, or graphs, consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs,…
Evaluating semantic models with word-sentence relatedness
Kimberly Glasgow, Matthew Roos, Amy Haufler +2
Semantic textual similarity (STS) systems are designed to encode and evaluate the semantic similarity between words, phrases, sentences, and documents. One method for assessing the…