| 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 | 25 | 2022 |
| Robustness of edited neural networks D Brown, C Godfrey, C Nizinski, J Tu, H Kvinge ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation …, 2023 | 11 | 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 |
| Pure subrings of Du Bois singularities are Du Bois singularities C Godfrey, T Murayama arXiv preprint arXiv:2208.14429, 2022 | 3 | 2022 |
| 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 |
| Neural Image Compression: Generalization, Robustness, and Spectral Biases K Lieberman, J Diffenderfer, C Godfrey, B Kailkhura Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
| Impact of model architecture on robustness and interpretability of multispectral deep learning models C Godfrey, E Bishoff, M McKay, E Byler Algorithms, Technologies, and Applications for Multispectral and …, 2023 | 1 | 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 | 1 | 2023 |
| Neural frames: A Tool for Studying the Tangent Bundles Underlying Image Datasets and How Deep Learning Models Process Them H Kvinge, G Jorgenson, D Brown, C Godfrey, T Emerson arXiv preprint arXiv:2211.10558, 2022 | 1 | 2022 |
| Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps E Coda, N Courts, C Wight, L Truong, WJ Choi, C Godfrey, T Emerson, ... Topological, Algebraic and Geometric Learning Workshops 2022, 79-85, 2022 | 1 | 2022 |
| Adapting self-supervised learning to the hyperspectral domain: methods, challenges, and lessons learned N Kono, EB Byler, CW Godfrey, TJ Doster, TH Emerson Algorithms, Technologies, and Applications for Multispectral and …, 2024 | | 2024 |
| 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, 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 |
| Gumby: Quantifying multi-modal model resiliency EB Byler, EA Bishoff, CW Godfrey, MA McKay Pacific Northwest National Laboratory (PNNL), Richland, WA (United States), 2023 | | 2023 |
| Impact of architecture on robustness and interpretability of multispectral deep neural networks C Godfrey, E Bishoff, M McKay, E Byler arXiv preprint arXiv:2309.12463, 2023 | | 2023 |
| Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning E Bishoff, C Godfrey, M McKay, E Byler Algorithms, Technologies, and Applications for Multispectral and …, 2023 | | 2023 |
| Correspondences in log Hodge cohomology C Godfrey arXiv preprint arXiv:2301.00517, 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 |
Henry KvingePacific Northwest National Lab在 pnnl.gov 的电子邮件经过验证
