20 citations · 25 across the 5 of their papers we have counts for
6 papers · 1 filter
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning
Henry Kvinge, Zachary New, Nico Courts +6
Deep learning has shown great success in settings with massive amounts of data but has struggled when data is limited. Few-shot learning algorithms, which seek to address this limi…
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…
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.…
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…
Dynamic Input Structure and Network Assembly for Few-Shot Learning
Nathan Hilliard, Nathan O. Hodas, Courtney D. Corley
The ability to learn from a small number of examples has been a difficult problem in machine learning since its inception. While methods have succeeded with large amounts of traini…
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems
Enoch Yeung, Soumya Kundu, Nathan Hodas
The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery. However, its application has been hindered by…