On the Symmetries of Deep Learning Models and their Internal Representations. - Charles Godfrey; Davis Brown; Tegan Emerson; Henry Kvinge (NeurIPS, 2022)

Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps. - Elizabeth Coda; Nico Courts; Colby Wight; Loc Truong; WoongJo Choi; Charles Godfrey; Tegan Emerson; Keerti Kappagantula; Henry Kvinge (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)

How many dimensions are required to find an adversarial example? - Charles Godfrey; Henry Kvinge; Elise Bishoff; Myles Mckay; Davis Brown; Tim Doster; Eleanor Byler (CVPR Workshops, 2023)

Understanding the Inner-workings of Language Models Through Representation Dissimilarity. - Davis Brown; Charles Godfrey; Nicholas Konz; Jonathan H. Tu; Henry Kvinge (EMNLP, 2023)

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)

Neural Image Compression: Generalization, Robustness, and Spectral Biases. - Kelsey Lieberman; James Diffenderfer; Charles Godfrey; Bhavya Kailkhura (NeurIPS, 2023)

Exploring the Representation Manifolds of Stable Diffusion Through the Lens of Intrinsic Dimension. - Henry Kvinge; Davis Brown; Charles Godfrey (CoRR, 2023)

Robustness of edited neural networks. - Davis Brown; Charles Godfrey; Cody Nizinski; Jonathan H. Tu; Henry Kvinge (CoRR, 2023)

Fast computation of permutation equivariant layers with the partition algebra. - Charles Godfrey; Michael G. Rawson; Davis Brown; Henry Kvinge (CoRR, 2023)

Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning. - Elise Bishoff; Charles Godfrey; Myles Mckay; Eleanor Byler (CoRR, 2023)

Impact of architecture on robustness and interpretability of multispectral deep neural networks. - Charles Godfrey; Elise Bishoff; Myles Mckay; Eleanor Byler (CoRR, 2023)

Attributing Learned Concepts in Neural Networks to Training Data. - Nicholas Konz; Charles Godfrey; Madelyn Shapiro; Jonathan H. Tu; Henry Kvinge; Davis Brown (CoRR, 2023)

Likert or Not: LLM Absolute Relevance Judgments on Fine-Grained Ordinal Scales. - Charles Godfrey; Ping Nie; Natalia Ostapuk; David Ken; Shang Gao; Souheil Inati (CoRR, 2025)