In What Ways Are Deep Neural Networks Invariant and How Should We Measure This? - Henry Kvinge; Tegan Emerson; Grayson Jorgenson; Scott Vasquez; Tim Doster; Jesse D. Lew (NeurIPS, 2022)

Random Filters for Enriching the Discriminatory power of Topological Representations. - Grayson Jorgenson; Henry Kvinge; Tegan Emerson; Colin C. Olson (TAG-ML, 2022)

Convolutional networks inherit frequency sensitivity from image statistics. - Charles Godfrey; Elise Bishoff; Myles Mckay; Davis Brown; Grayson Jorgenson; Henry Kvinge; Eleanor Byler (CoRR, 2022)

Neural frames: A Tool for Studying the Tangent Bundles Underlying Image Datasets and How Deep Learning Models Process Them. - Henry Kvinge; Grayson Jorgenson; Davis Brown; Charles Godfrey; Tegan Emerson (CoRR, 2022)

Internal representations of vision models through the lens of frames on data manifolds. - Henry Kvinge; Grayson Jorgenson; Davis Brown; Charles Godfrey; Tegan Emerson (NeurReps, 2023)

Haldane bundles: a dataset for learning to predict the Chern number of line bundles on the torus. - Cody Tipton; Elizabeth Coda; Davis Brown; Alyson Bittner; Jung Lee; Grayson Jorgenson; Tegan Emerson; Henry Kvinge (NeurReps, 2023)

ColMix - A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images. - Cuong Ly; Grayson Jorgenson; Dan Rosa de Jesus; Henry Kvinge; Adam Attarian; Yijing Watkins (CoRR, 2023)

Haldane Bundles: A Dataset for Learning to Predict the Chern Number of Line Bundles on the Torus. - Cody Tipton; Elizabeth Coda; Davis Brown; Alyson Bittner; Jung H. Lee; Grayson Jorgenson; Tegan Emerson; Henry Kvinge (CoRR, 2023)