6 citations · 25 across the 41 of their papers we have counts for
7 papers · 1 filter
Do Neural Networks Trained with Topological Features Learn Different Internal Representations?
Sarah McGuire, Shane Jackson, Tegan Emerson +1
There is a growing body of work that leverages features extracted via topological data analysis to train machine learning models. While this field, sometimes known as topological m…
Internal Representations of Vision Models Through the Lens of Frames on Data Manifolds
Henry Kvinge, Grayson Jorgenson, Davis Brown +2
While the last five years have seen considerable progress in understanding the internal representations of deep learning models, many questions remain. This is especially true when…
In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?
Henry Kvinge, Tegan H. Emerson, Grayson Jorgenson +3
It is often said that a deep learning model is "invariant" to some specific type of transformation. However, what is meant by this statement strongly depends on the context in whic…
Testing predictions of representation cost theory with CNNs
Charles Godfrey, Elise Bishoff, Myles Mckay +4
It is widely acknowledged that trained convolutional neural networks (CNNs) have different levels of sensitivity to signals of different frequency. In particular, a number of empir…
TopTemp: Parsing Precipitate Structure from Temper Topology
Lara Kassab, Scott Howland, Henry Kvinge +2
Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements. Development and ev…
On the Symmetries of Deep Learning Models and their Internal Representations
Charles Godfrey, Davis Brown, Tegan Emerson +1
Symmetry is a fundamental tool in the exploration of a broad range of complex systems. In machine learning symmetry has been explored in both models and data. In this paper we seek…