Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large datasets. - Henry Kvinge; Elin Farnell; Michael Kirby; Chris Peterson 001 (IEEE BigData, 2018)

Endmember Extraction on the Grassmannian. - Elin Farnell; Henry Kvinge; Michael Kirby; Chris Peterson 001 (DSW, 2018)

Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets. - Henry Kvinge; Elin Farnell; Michael Kirby; Chris Peterson 001 (HPEC, 2018)

A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction. - Henry Kvinge; Elin Farnell; Michael Kirby; Chris Peterson 001 (ISPDC, 2018)

Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets. - Henry Kvinge; Elin Farnell; Michael Kirby; Chris Peterson 001 (CoRR, 2018)

Letting symmetry guide visualization: multidimensional scaling on groups. - Henry Kvinge; Mark Blumstein (CoRR, 2018)

Rare Geometries: Revealing Rare Categories via Dimension-Driven Statistics. - Henry Kvinge; Elin Farnell; Jingya Li; Yujia Chen (ICMLA, 2019)

A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data. - Henry Kvinge; Michael Kirby; Chris Peterson 001; Chad Eitel; Tod Clapp (WSOM+, 2019)

Rare geometries: revealing rare categories via dimension-driven statistics. - Henry Kvinge; Elin Farnell (CoRR, 2019)

A data-driven approach to sampling matrix selection for compressive sensing. - Elin Farnell; Henry Kvinge; John P. Dixon; Julia R. Dupuis; Michael Kirby; Chris Peterson 001; Elizabeth C. Schundler; Christian W. Smith (CoRR, 2019)

More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing. - Henry Kvinge; Elin Farnell; Julia R. Dupuis; Michael Kirby; Chris Peterson 001; Elizabeth C. Schundler (CoRR, 2019)

Rotational Equivariance for Object Classification Using xView. - Lucius Bynum; Timothy Doster; Tegan H. Emerson; Henry Kvinge (IGARSS, 2020)

Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning. - Henry Kvinge; Zachary New; Nico Courts; Jung H. Lee; Lauren A. Phillips; Courtney D. Corley; Aaron Tuor; Andrew Avila; Nathan O. Hodas (MetaDL@AAAI, 2021)

Multi-Dimensional Scaling on Groups. - Mark Blumstein; Henry Kvinge (ICCVW, 2021)

Sheaves as a Framework for Understanding and Interpreting Model Fit. - Henry Kvinge; Brett A. Jefferson; Cliff A. Joslyn; Emilie Purvine (ICCVW, 2021)

Hypergraph models of biological networks to identify genes critical to pathogenic viral response. - Song Feng; Emily Heath; Brett A. Jefferson; Cliff A. Joslyn; Henry Kvinge; Hugh D. Mitchell; Brenda Praggastis; Amie J. Eisfeld; Amy C. Sims; Larissa B. Thackray; Shufang Fan; Kevin B. Walters; et al. (BMC Bioinform., 2021)

Prototypical Region Proposal Networks for Few-Shot Localization and Classification. - Elliott Skomski; Aaron Tuor; Andrew Avila; Lauren A. Phillips; Zachary New; Henry Kvinge; Courtney D. Corley; Nathan O. Hodas (CoRR, 2021)

One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations. - Henry Kvinge; Scott Howland; Nico Courts; Lauren A. Phillips; John Buckheit; Zachary New; Elliott Skomski; Jung H. Lee; Sandeep Tiwari; Jessica Hibler; Courtney D. Corley; Nathan O. Hodas (CoRR, 2021)

Rotating spiders and reflecting dogs: a class conditional approach to learning data augmentation distributions. - Scott Mahan; Henry Kvinge; Tim Doster (CoRR, 2021)

A Topological-Framework to Improve Analysis of Machine Learning Model Performance. - Henry Kvinge; Colby Wight; Sarah Akers; Scott Howland; Woongjo Choi; Xiaolong Ma; Luke J. Gosink; Elizabeth Jurrus; Keerti Kappagantula; Tegan H. Emerson (CoRR, 2021)

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

Adaptive Transfer Learning: a simple but effective transfer learning. - Jung H. Lee; Henry J. Kvinge; Scott Howland; Zachary New; John Buckheit; Lauren A. Phillips; Elliott Skomski; Jessica Hibler; Courtney D. Corley; Nathan O. Hodas (CoRR, 2021)

Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing. - Loc Truong; WoongJo Choi; Colby Wight; Lizzy Coda; Tegan Emerson; Keerti Kappagantula; Henry Kvinge (CoRR, 2021)

DNA: Dynamic Network Augmentation. - Scott Mahan; Tim Doster; Henry Kvinge (CoRR, 2021)

Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps. - Nico Courts; Henry Kvinge (ICLR, 2022)

Do neural networks trained with topological features learn different internal representations? - Sarah McGuire; Shane Jackson; Tegan Emerson; Henry Kvinge (NeurReps, 2022)

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

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)

Preface. - Alexander Cloninger; Timothy Doster; Tegan Emerson; Manohar Kaul; Ira Ktena; Henry Kvinge; Nina Miolane; Bastian Rice; Sarah Tymochko; Guy Wolf (TAG-ML, 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)

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

TopTemp: Parsing Precipitate Structure from Temper Topology. - Lara Kassab; Scott Howland; Henry Kvinge; Keerti Sahithi Kappagantula; Tegan Emerson (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)

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)

TopFusion: Using Topological Feature Space for Fusion and Imputation in Multi-Modal Data. - Audun Myers; Henry Kvinge; Tegan Emerson (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)

Preface. - Timothy Doster; Tegan Emerson; Henry Kvinge; Nina Miolane; Mathilde Papillon; Bastian Rieck; Sophia Sanborn (TAG-ML, 2023)

ICML 2023 Topological Deep Learning Challenge: Design and Results. - Mathilde Papillon; Mustafa Hajij; Audun Myers; Florian Frantzen; Ghada Zamzmi; Helen Jenne; Johan Mathe; Josef Hoppe; Michael T. Schaub; Theodore Papamarkou; Aldo Guzmán-Sáenz; Bastian Rieck; et al. (TAG-ML, 2023)

Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing. - Scott Howland; Lara Kassab; Keerti Kappagantula; Henry Kvinge; Tegan Emerson (CoRR, 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)

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)

SCITUNE: Aligning Large Language Models with Scientific Multimodal Instructions. - Sameera Horawalavithana; Sai Munikoti; Ian Stewart; Henry Kvinge (CoRR, 2023)

ICML 2023 Topological Deep Learning Challenge : Design and Results. - Mathilde Papillon; Mustafa Hajij; Florian Frantzen; Josef Hoppe; Helen Jenne; Johan Mathe; Audun Myers; Theodore Papamarkou; Michael T. Schaub; Ghada Zamzmi; Tolga Birdal; Tamal K. Dey; et al. (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)

Generalist Multimodal AI: A Review of Architectures, Challenges and Opportunities. - Sai Munikoti; Ian Stewart; Sameera Horawalavithana; Henry Kvinge; Tegan Emerson; Sandra E. Thompson; Karl Pazdernik (CoRR, 2024)

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)

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain. - Guillermo Bernárdez; Lev Telyatnikov; Marco Montagna; Federica Baccini; Mathilde Papillon; Miquel Ferriol Galmés; Mustafa Hajij; Theodore Papamarkou; Maria Sofia Bucarelli; Olga Zaghen; Johan Mathe; Audun Myers; et al. (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)

Foundation Models for Remote Sensing: An Analysis of MLLMs for Object Localization. - Darryl Hannan; John Cooper; Dylan White; Timothy Doster; Henry Kvinge; Yijing Watkins (CVPR Workshops, 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)

FMG-Det: Foundation Model Guided Robust Object Detection. - Darryl Hannan; Timothy Doster; Henry Kvinge; Adam Attarian; Yijing Watkins (CoRR, 2025)

Automating Evaluation of Diffusion Model Unlearning with (Vision-) Language Model World Knowledge. - Eric Yeats; Darryl Hannan; Henry Kvinge; Timothy Doster; Scott Mahan (CoRR, 2025)