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 | 115 | 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 | 26 | 2022 |
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 | 11* | 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 | 11 | 2022 |
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 | 10* | 2023 |
Experimental observations of the topology of convolutional neural network activations E Purvine, D Brown, B Jefferson, C Joslyn, B Praggastis, A Rathore, ... Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 9470-9479, 2023 | 8 | 2023 |
On privileged and convergent bases in neural network representations D Brown, N Vyas, Y Bansal arXiv preprint arXiv:2307.12941, 2023 | 4 | 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 | 4 | 2023 |
Understanding the inner workings of language models through representation dissimilarity D Brown, C Godfrey, N Konz, J Tu, H Kvinge arXiv preprint arXiv:2310.14993, 2023 | 3 | 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 | 3 | 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 | 2 | 2023 |
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 | 1 | 2023 |
Internal Representations of Vision Models Through the Lens of Frames on Data Manifolds H Kvinge, G Jorgenson, D Brown, C Godfrey, T Emerson NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations, 2022 | 1* | 2022 |
Model editing for distribution shifts in uranium oxide morphological analysis D Brown, C Nizinski, M Shapiro, C Fallon, T Yin, H Kvinge, JH Tu arXiv preprint arXiv:2407.15756, 2024 | | 2024 |
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 | | 2024 |
Haldane Bundles: A Dataset for Learning to Predict the Chern Number of Line Bundles on the Torus C Tipton, E Coda, D Brown, A Bittner, J Lee, G Jorgenson, T Emerson, ... arXiv preprint arXiv:2312.04600, 2023 | | 2023 |
DeepDataProfiler: A Platform and Methodology for the Analysis and Interpretation of Neural Networks BL Praggastis, DR Brown, EAH Purvine, MR Shapiro, B Wang Pacific Northwest National Laboratory (PNNL), Richland, WA (United States), 2023 | | 2023 |
Attributing Learned Concepts in Neural Networks to Training Data N Konz, C Godfrey, M Shapiro, J Tu, H Kvinge, D Brown arXiv preprint arXiv:2310.03149, 2023 | | 2023 |
Testing predictions of representation cost theory with CNNs C Godfrey, E Bishoff, M Mckay, D Brown, G Jorgenson, H Kvinge, E Byler arXiv preprint arXiv:2210.01257, 2022 | | 2022 |
Convolutional networks inherit frequency sensitivity from image statistics. C Godfrey, E Bishoff, M Mckay, D Brown, G Jorgenson, H Kvinge, E Byler CoRR, 2022 | | 2022 |