Brittle interpretations: The Vulnerability of TCAV and Other Concept-based Explainability Tools to Adversarial Attack. - Davis Brown; Henry Kvinge (CoRR, 2021)

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

The SVD of Convolutional Weights: A CNN Interpretability Framework. - Brenda Praggastis; Davis Brown; Carlos Ortiz Marrero; Emilie Purvine; Madelyn Shapiro; Bei Wang 001 (CoRR, 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)

Experimental Observations of the Topology of Convolutional Neural Network Activations. - Emilie Purvine; Davis Brown; Brett A. Jefferson; Cliff A. Joslyn; Brenda Praggastis; Archit Rathore; Madelyn Shapiro; Bei Wang 001; Youjia Zhou (AAAI, 2023)

Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools. - Davis Brown; Henry Kvinge (CVPR Workshops, 2023)

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)

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)

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)

On Privileged and Convergent Bases in Neural Network Representations. - Davis Brown; Nikhil Vyas 001; Yamini Bansal (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)

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)

Model editing for distribution shifts in uranium oxide morphological analysis. - Davis Brown; Cody Nizinski; Madelyn Shapiro; Corey Fallon; Tianzhixi Yin; Henry Kvinge; Jonathan H. Tu (CoRR, 2024)

Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes. - Jesse He; Helen Jenne; Herman Chau; Davis Brown; Mark Raugas; Sara C. Billey; Henry Kvinge (CoRR, 2024)

Adaptively evaluating models with task elicitation. - Davis Brown; Prithvi Balehannina; Helen Jin; Shreya Havaldar; Hamed Hassani; Eric Wong 001 (CoRR, 2025)

Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics. - Herman Chau; Helen Jenne; Davis Brown; Jesse He; Mark Raugas; Sara C. Billey; Henry Kvinge (CoRR, 2025)

Benchmarking Misuse Mitigation Against Covert Adversaries. - Davis Brown; Mahdi Sabbaghi; Luze Sun; Alexander Robey; George J. Pappas; Eric Wong 001; Hamed Hassani (CoRR, 2025)