4 citations · 8 across the 7 of their papers we have counts for
8 papers
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…
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…
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…
Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps
Elizabeth Coda, Nico Courts, Colby Wight +6
While it is not generally reflected in the `nice' datasets used for benchmarking machine learning algorithms, the real-world is full of processes that would be best described as ma…
A Topological-Framework to Improve Analysis of Machine Learning Model Performance
Henry Kvinge, Colby Wight, Sarah Akers +7
As both machine learning models and the datasets on which they are evaluated have grown in size and complexity, the practice of using a few summary statistics to understand model p…
A Topological Approach for Motion Track Discrimination
Tegan Emerson, Sarah Tymochko, George Stantchev +3
Detecting small targets at range is difficult because there is not enough spatial information present in an image sub-region containing the target to use correlation-based methods…