| Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning V Sagan, M Maimaitijiang, S Bhadra, M Maimaitiyiming, DR Brown, ... ISPRS journal of photogrammetry and remote sensing 174, 265-281, 2021 | 171 | 2021 |
| On the symmetries of deep learning models and their internal representations C Godfrey*, D Brown*, T Emerson, H Kvinge Advances in Neural Information Processing Systems 35, 11893-11905, 2022 | 54 | 2022 |
| Experimental observations of the topology of convolutional neural network activations E Purvine, D Brown, B Jefferson, C Joslyn, B Praggastis, A Rathore, ... AAAI Conference on Artificial Intelligence 37 (8), 9470-9479, 2023 | 24 | 2023 |
| Making corgis important for honeycomb classification: Adversarial attacks on concept-based explainability tools D Brown, H Kvinge Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 22* | 2023 |
| Edit at your own risk: evaluating the robustness of edited models to distribution shifts D Brown*, C Godfrey*, C Nizinski, J Tu, H Kvinge arXiv preprint arXiv:2303.00046, 2023 | 18* | 2023 |
| The SVD of convolutional weights: A CNN interpretability framework B Praggastis, D Brown, CO Marrero, E Purvine, M Shapiro, B Wang arXiv preprint arXiv:2208.06894, 2022 | 17 | 2022 |
| Understanding the inner workings of language models through representation dissimilarity D Brown, C Godfrey, N Konz, J Tu, H Kvinge EMNLP 2023, 2023 | 14 | 2023 |
| How many dimensions are required to find an adversarial example? C Godfrey, H Kvinge, E Bishoff, M Mckay, D Brown, T Doster, E Byler Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 10 | 2023 |
| Fast computation of permutation equivariant layers with the partition algebra C Godfrey, MG Rawson, D Brown, H Kvinge arXiv preprint arXiv:2303.06208, 2023 | 8 | 2023 |
| Exploring the representation manifolds of stable diffusion through the lens of intrinsic dimension H Kvinge, D Brown, C Godfrey arXiv preprint arXiv:2302.09301, 2023 | 7 | 2023 |
| Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics H Chau*, H Jenne*, D Brown*, J He, M Raugas, SC Billey, H Kvinge ICML 2025 (oral), 2025 | 6 | 2025 |
| On privileged and convergent bases in neural network representations D Brown, N Vyas, Y Bansal arXiv preprint arXiv:2307.12941, 2023 | 6 | 2023 |
| Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes J He, H Jenne, H Chau, D Brown, M Raugas, S Billey, H Kvinge ICML 2025, 2024 | 5 | 2024 |
| Internal representations of vision models through the lens of frames on data manifolds H Kvinge, G Jorgenson, D Brown, C Godfrey, T Emerson arXiv preprint arXiv:2211.10558, 2022 | 5* | 2022 |
| Wild comparisons: A study of how representation similarity changes when input data is drawn from a shifted distribution D Brown, MR Shapiro, A Bittner, J Warley, H Kvinge ICLR 2024 Workshop on Representational Alignment, 2024 | 4 | 2024 |
| Comparing Mapper graphs of artificial neuron activations Y Zhou, H Jenne, D Brown, M Shapiro, B Jefferson, C Joslyn, ... 2023 Topological Data Analysis and Visualization (TopoInVis), 41-50, 2023 | 4 | 2023 |
| Probing the limits of mathematical world models in LLMs H Kvinge, E Coda, E Yeats, D Brown, J Buckheit, SMG Scullen, ... ICML 2025 Workshop on Assessing World Models, 2025 | 3 | 2025 |
| Attributing Learned Concepts in Neural Networks to Training Data N Konz, C Godfrey, M Shapiro, J Tu, H Kvinge, D Brown ATTRIB @ NeurIPS (Oral), 2023 | 3 | 2023 |
| Adaptively profiling models with task elicitation D Brown, P Balehannina, H Jin, S Havaldar, H Hassani, E Wong EMNLP 2025, 2025 | 2* | 2025 |
| What do Geometric Hallucination Detection Metrics Actually Measure? E Yeats, J Buckheit, SMG Scullen, B Kennedy, L Truong, D Brown, B Kay, ... ICML 2025 Workshop on Reliable and Responsible Foundation Models, 2025 | 2 | 2025 |
Henry KvingePacific Northwest National Lab/University of WashingtonVerified email at pnnl.gov
