136 citations · 231 across the 11 of their papers we have counts for
5 papers · 1 filter
The Outer Product Structure of Neural Network Derivatives
Craig Bakker, Michael J. Henry, Nathan O. Hodas
In this paper, we show that feedforward and recurrent neural networks exhibit an outer product derivative structure but that convolutional neural networks do not. This structure ma…
Model of Cognitive Dynamics Predicts Performance on Standardized Tests
Nathan O. Hodas, Jacob Hunter, Stephen J. Young +1
In the modern knowledge economy, success demands sustained focus and high cognitive performance. Research suggests that human cognition is linked to a finite resource, and upon its…
Doing the impossible: Why neural networks can be trained at all
Nathan O. Hodas, Panos Stinis
As deep neural networks grow in size, from thousands to millions to billions of weights, the performance of those networks becomes limited by our ability to accurately train them.…
Sharkzor: Interactive Deep Learning for Image Triage, Sort and Summary
Meg Pirrung, Nathan Hilliard, Artëm Yankov +4
Sharkzor is a web application for machine-learning assisted image sort and summary. Deep learning algorithms are leveraged to infer, augment, and automate the user's mental model.…
Few-Shot Learning with Metric-Agnostic Conditional Embeddings
Nathan Hilliard, Lawrence Phillips, Scott Howland +3
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning. To accomplish this, we introduce a novel architec…