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ORCIDHenry Kvinge
Henry J. Kvinge
Person information
- affiliation: Pacific Northwest National Laboratory, Richland, WA, USA
- affiliation: University of Washington, Department of Mathematics, Seattle, WA, USA
- affiliation: Colorado State University, Fort Collins, CO, USA
- affiliation (PhD): University of California Davis, CA, USA
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2020 – today
- 2023
[c24]Audun Myers, Henry Kvinge, Tegan Emerson:
TopFusion: Using Topological Feature Space for Fusion and Imputation in Multi-Modal Data. CVPR Workshops 2023: 600-609- [c23]Davis Brown, Henry Kvinge:
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools. CVPR Workshops 2023: 620-627 - [c22]Charles Godfrey, Henry Kvinge, Elise Bishoff, Myles Mckay, Davis Brown, Tim Doster, Eleanor Byler:
How many dimensions are required to find an adversarial example? CVPR Workshops 2023: 2353-2360 - [c21]Davis Brown, Charles Godfrey, Nicholas Konz, Jonathan Tu, Henry Kvinge:
Understanding the Inner-workings of Language Models Through Representation Dissimilarity. EMNLP 2023: 6543-6558 - [c20]Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn:
Preface. TAG-ML 2023: 1-2 - [c19]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, Neal Livesay, Tamal K. Dey, Abraham Rabinowitz, Aiden Brent, Alessandro Salatiello, Alexander Nikitin, Ali Zia, Claudio Battiloro, Dmitrii Gavrilev, Georg Bökman, German Magai, Gleb Bazhenov, Guillermo Bernárdez, Indro Spinelli, Jens Agerberg, Kalyan Varma Nadimpalli, Lev Telyatnikov, Luca Scofano, Lucia Testa, Manuel Lecha, Maosheng Yang, Mohammed Hassanin, Odin Hoff Gardaa, Olga Zaghen, Paul Häusner, Paul Snopoff, Pavlo Melnyk, Rubén Ballester, Sadrodin Barikbin, Sergio Escalera, Simone Fiorellino, Henry Kvinge, Jan Meissner, Karthikeyan Natesan Ramamurthy, Michael Scholkemper, Paul Rosen, Robin Walters, Shreyas N. Samaga, Soham Mukherjee, Sophia Sanborn, Tegan Emerson, Timothy Doster, Tolga Birdal, Vincent P. Grande, Abdelwahed Khamis, Simone Scardapane, Suraj Singh, Tatiana Malygina, Yixiao Yue, Nina Miolane:
ICML 2023 Topological Deep Learning Challenge: Design and Results. TAG-ML 2023: 3-8 - [e2]Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn:
Topological, Algebraic and Geometric Learning Workshops 2023, 28 July 2023, Honolulu, HI, USA. Proceedings of Machine Learning Research 221, PMLR 2023 [contents] - [i37]Scott Howland, Lara Kassab, Keerti Kappagantula, Henry Kvinge, Tegan Emerson:
Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing. CoRR abs/2302.08495 (2023) - [i36]Henry Kvinge, Davis Brown, Charles Godfrey:
Exploring the Representation Manifolds of Stable Diffusion Through the Lens of Intrinsic Dimension. CoRR abs/2302.09301 (2023) - [i35]Davis Brown, Charles Godfrey, Cody Nizinski, Jonathan Tu, Henry Kvinge:
Robustness of edited neural networks. CoRR abs/2303.00046 (2023) - [i34]Charles Godfrey, Michael G. Rawson, Davis Brown, Henry Kvinge:
Fast computation of permutation equivariant layers with the partition algebra. CoRR abs/2303.06208 (2023) - [i33]Charles Godfrey, Henry Kvinge, Elise Bishoff, Myles Mckay, Davis Brown, Tim Doster, Eleanor Byler:
How many dimensions are required to find an adversarial example? CoRR abs/2303.14173 (2023) - [i32]Cuong Ly, Grayson Jorgenson, Dan Rosa de Jesus, Henry Kvinge, Adam Attarian, Yijing Watkins:
ColMix - A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images. CoRR abs/2305.13509 (2023) - [i31]Sameera Horawalavithana, Sai Munikoti, Ian Stewart, Henry Kvinge:
SCITUNE: Aligning Large Language Models with Scientific Multimodal Instructions. CoRR abs/2307.01139 (2023) - [i30]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, Tim Doster, Tegan Emerson, Gurusankar Gopalakrishnan, Devendra Govil, Vincent P. Grande, Aldo Guzmán-Sáenz, Henry Kvinge, Neal Livesay, Jan Meissner, Soham Mukherjee, Shreyas N. Samaga, Karthikeyan Natesan Ramamurthy, Maneel Reddy Karri, Paul Rosen, Sophia Sanborn, Michael Scholkemper, Robin Walters, Jens Agerberg, Georg Bökman, Sadrodin Barikbin, Claudio Battiloro, Gleb Bazhenov, Guillermo Bernárdez, Aiden Brent, Sergio Escalera, Simone Fiorellino, Dmitrii Gavrilev, Mohammed Hassanin, Paul Häusner, Odin Hoff Gardaa, Abdelwahed Khamis, Manuel Lecha, German Magai, Tatiana Malygina, Pavlo Melnyk, et al.:
ICML 2023 Topological Deep Learning Challenge : Design and Results. CoRR abs/2309.15188 (2023) - [i29]Nicholas Konz, Charles Godfrey, Madelyn Shapiro, Jonathan Tu, Henry Kvinge, Davis Brown:
Attributing Learned Concepts in Neural Networks to Training Data. CoRR abs/2310.03149 (2023) - [i28]Davis Brown, Charles Godfrey, Nicholas Konz, Jonathan Tu, Henry Kvinge:
Understanding the Inner Workings of Language Models Through Representation Dissimilarity. CoRR abs/2310.14993 (2023) - [i27]Cody Tipton, Elizabeth Coda, Davis Brown, Alyson Bittner, Jung H. Lee, Grayson Jorgenson, Tegan Emerson, Henry Kvinge:
Haldane Bundles: A Dataset for Learning to Predict the Chern Number of Line Bundles on the Torus. CoRR abs/2312.04600 (2023) - 2022
- [c18]Nico Courts, Henry Kvinge:
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps. ICLR 2022 - [c17]Sarah McGuire, Shane Jackson, Tegan Emerson, Henry Kvinge:
Do neural networks trained with topological features learn different internal representations? NeurReps 2022: 122-136 - [c16]Charles Godfrey, Davis Brown, Tegan Emerson, Henry Kvinge:
On the Symmetries of Deep Learning Models and their Internal Representations. NeurIPS 2022 - [c15]Henry Kvinge, Tegan Emerson, Grayson Jorgenson, Scott Vasquez, Tim Doster, Jesse D. Lew:
In What Ways Are Deep Neural Networks Invariant and How Should We Measure This? NeurIPS 2022 - [c14]Alexander Cloninger, Timothy Doster, Tegan Emerson, Manohar Kaul, Ira Ktena, Henry Kvinge, Nina Miolane, Bastian Rice, Sarah Tymochko, Guy Wolf:
Preface. TAG-ML 2022: 1-5 - [c13]Elizabeth Coda, Nico Courts, Colby Wight, Loc Truong, WoongJo Choi, Charles Godfrey, Tegan Emerson, Keerti Kappagantula, Henry Kvinge:
Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps. TAG-ML 2022: 79-85 - [c12]Grayson Jorgenson, Henry Kvinge, Tegan Emerson, Colin C. Olson:
Random Filters for Enriching the Discriminatory power of Topological Representations. TAG-ML 2022: 183-188 - [c11]Lara Kassab, Scott Howland, Henry Kvinge, Keerti Sahithi Kappagantula, Tegan Emerson:
TopTemp: Parsing Precipitate Structure from Temper Topology. TAG-ML 2022: 199-205 - [e1]Alexander Cloninger, Timothy Doster, Tegan Emerson, Manohar Kaul, Ira Ktena, Henry Kvinge, Nina Miolane, Bastian Rice, Sarah Tymochko, Guy Wolf:
Topological, Algebraic and Geometric Learning Workshops 2022, 25-22 July 2022, Virtual. Proceedings of Machine Learning Research 196, PMLR 2022 [contents] - [i26]Elizabeth Coda, Nico Courts, Colby Wight, Loc Truong, WoongJo Choi, Charles Godfrey, Tegan Emerson, Keerti Kappagantula, Henry Kvinge:
Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps. CoRR abs/2203.08189 (2022) - [i25]Lara Kassab, Scott Howland, Henry Kvinge, Keerti Sahithi Kappagantula, Tegan Emerson:
TopTemp: Parsing Precipitate Structure from Temper Topology. CoRR abs/2204.00629 (2022) - [i24]Charles Godfrey
, Davis Brown, Tegan Emerson, Henry Kvinge:
On the Symmetries of Deep Learning Models and their Internal Representations. CoRR abs/2205.14258 (2022) - [i23]Charles Godfrey, Elise Bishoff, Myles Mckay, Davis Brown, Grayson Jorgenson, Henry Kvinge, Eleanor Byler:
Convolutional networks inherit frequency sensitivity from image statistics. CoRR abs/2210.01257 (2022) - [i22]Henry Kvinge, Tegan H. Emerson, Grayson Jorgenson, Scott Vasquez, Timothy Doster, Jesse D. Lew:
In What Ways Are Deep Neural Networks Invariant and How Should We Measure This? CoRR abs/2210.03773 (2022) - [i21]Sarah McGuire, Shane Jackson, Tegan Emerson, Henry Kvinge:
Do Neural Networks Trained with Topological Features Learn Different Internal Representations? CoRR abs/2211.07697 (2022) - [i20]Henry Kvinge, Grayson Jorgenson, Davis Brown, Charles Godfrey, Tegan Emerson:
Neural frames: A Tool for Studying the Tangent Bundles Underlying Image Datasets and How Deep Learning Models Process Them. CoRR abs/2211.10558 (2022) - 2021
- [j1]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, Peter J. Halfmann, Danielle Westhoff-Smith, Qing Tan, Vineet D. Menachery, Timothy P. Sheahan, Adam S. Cockrell, Jacob F. Kocher, Kelly G. Stratton, Natalie C. Heller, Lisa M. Bramer, Michael S. Diamond, Ralph S. Baric, Katrina M. Waters, Yoshihiro Kawaoka, Jason E. McDermott, Emilie Purvine:
Hypergraph models of biological networks to identify genes critical to pathogenic viral response. BMC Bioinform. 22(1): 287 (2021) - [c10]Henry Kvinge, Zachary New, Nico Courts, Jung H. Lee, Lauren A. Phillips, Courtney D. Corley, Aaron Tuor, Andrew Avila, Nathan O. Hodas:
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning. MetaDL@AAAI 2021: 77-89 - [c9]Henry Kvinge, Brett A. Jefferson, Cliff A. Joslyn, Emilie Purvine:
Sheaves as a Framework for Understanding and Interpreting Model Fit. ICCVW 2021: 4205-4213 - [c8]Mark Blumstein, Henry Kvinge:
Multi-Dimensional Scaling on Groups. ICCVW 2021: 4222-4227 - [i19]Elliott Skomski, Aaron Tuor, Andrew Avila, Lauren A. Phillips, Zachary New, Henry Kvinge, Courtney D. Corley, Nathan O. Hodas:
Prototypical Region Proposal Networks for Few-Shot Localization and Classification. CoRR abs/2104.03496 (2021) - [i18]Henry Kvinge, Brett A. Jefferson, Cliff A. Joslyn, Emilie Purvine:
Sheaves as a Framework for Understanding and Interpreting Model Fit. CoRR abs/2105.10414 (2021) - [i17]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:
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations. CoRR abs/2106.01423 (2021) - [i16]Scott Mahan, Henry Kvinge, Tim Doster:
Rotating spiders and reflecting dogs: a class conditional approach to learning data augmentation distributions. CoRR abs/2106.04009 (2021) - [i15]Henry Kvinge, Colby Wight, Sarah Akers, Scott Howland, Woongjo Choi, Xiaolong Ma, Luke J. Gosink, Elizabeth Jurrus, Keerti Kappagantula, Tegan H. Emerson:
A Topological-Framework to Improve Analysis of Machine Learning Model Performance. CoRR abs/2107.04714 (2021) - [i14]Nico Courts, Henry Kvinge:
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps. CoRR abs/2110.06983 (2021) - [i13]Davis Brown, Henry Kvinge:
Brittle interpretations: The Vulnerability of TCAV and Other Concept-based Explainability Tools to Adversarial Attack. CoRR abs/2110.07120 (2021) - [i12]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:
Adaptive Transfer Learning: a simple but effective transfer learning. CoRR abs/2111.10937 (2021) - [i11]Loc Truong, WoongJo Choi, Colby Wight, Lizzy Coda, Tegan Emerson, Keerti Kappagantula, Henry Kvinge:
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing. CoRR abs/2112.01687 (2021) - [i10]Scott Mahan, Tim Doster, Henry Kvinge:
DNA: Dynamic Network Augmentation. CoRR abs/2112.09277 (2021) - 2020
- [c7]Lucius Bynum, Timothy Doster, Tegan H. Emerson, Henry Kvinge:
Rotational Equivariance for Object Classification Using xView. IGARSS 2020: 3684-3687 - [i9]Henry Kvinge, Zachary New, Nico Courts, Jung H. Lee, Lauren A. Phillips, Courtney D. Corley, Aaron Tuor, Andrew Avila, Nathan O. Hodas:
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning. CoRR abs/2009.11253 (2020)
2010 – 2019
- 2019
- [c6]Henry Kvinge, Elin Farnell, Jingya Li, Yujia Chen:
Rare Geometries: Revealing Rare Categories via Dimension-Driven Statistics. ICMLA 2019: 276-281 - [c5]Henry Kvinge, Michael Kirby, Chris Peterson, Chad Eitel, Tod Clapp:
A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data. WSOM+ 2019: 160-165 - [i8]Henry Kvinge, Elin Farnell:
Rare geometries: revealing rare categories via dimension-driven statistics. CoRR abs/1901.10585 (2019) - [i7]Elin Farnell, Henry Kvinge, John P. Dixon, Julia R. Dupuis, Michael Kirby, Chris Peterson, Elizabeth C. Schundler, Christian W. Smith:
A data-driven approach to sampling matrix selection for compressive sensing. CoRR abs/1906.08869 (2019) - [i6]Henry Kvinge, Elin Farnell, Julia R. Dupuis, Michael Kirby, Chris Peterson, Elizabeth C. Schundler:
More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing. CoRR abs/1906.11818 (2019) - 2018
- [c4]Henry Kvinge, Elin Farnell, Michael Kirby, Chris Peterson:
Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large datasets. IEEE BigData 2018: 1045-1051 - [c3]Elin Farnell, Henry Kvinge, Michael Kirby, Chris Peterson:
Endmember Extraction on the Grassmannian. DSW 2018: 71-75 - [c2]Henry Kvinge, Elin Farnell, Michael Kirby, Chris Peterson:
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets. HPEC 2018: 1-7 - [c1]Henry Kvinge, Elin Farnell, Michael Kirby, Chris Peterson:
A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction. ISPDC 2018: 69-76 - [i5]Elin Farnell, Henry Kvinge, Michael Kirby, Chris Peterson:
Endmember Extraction on the Grassmannian. CoRR abs/1807.01401 (2018) - [i4]Henry Kvinge, Elin Farnell, Michael Kirby, Chris Peterson:
A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction. CoRR abs/1807.03425 (2018) - [i3]Henry Kvinge, Elin Farnell, Michael Kirby, Chris Peterson:
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets. CoRR abs/1808.01686 (2018) - [i2]Henry Kvinge, Elin Farnell, Michael Kirby, Chris Peterson:
Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets. CoRR abs/1810.11562 (2018) - [i1]Henry Kvinge, Mark Blumstein:
Letting symmetry guide visualization: multidimensional scaling on groups. CoRR abs/1812.03362 (2018)
Coauthor Index
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