| The hyperdimensional stack machine T Yerxa, A Anderson, E Weiss Cognitive Computing, 1-2, 2018 | 23 | 2018 |
| Efficient sensory coding of multidimensional stimuli TE Yerxa, E Kee, MR DeWeese, EA Cooper PLoS computational biology 16 (9), e1008146, 2020 | 18 | 2020 |
| Learning efficient coding of natural images with maximum manifold capacity representations T Yerxa, Y Kuang, E Simoncelli, SY Chung Advances in Neural Information Processing Systems 36, 24103-24128, 2023 | 10 | 2023 |
| Plenoptic: A platform for synthesizing model-optimized visual stimuli L Duong, K Bonnen, W Broderick, PÉ Fiquet, N Parthasarathy, T Yerxa, ... Journal of Vision 23 (9), 5822-5822, 2023 | 2 | 2023 |
| Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations R Schaeffer, V Lecomte, DB Pai, A Carranza, B Isik, A Unell, M Khona, ... arXiv preprint arXiv:2406.09366, 2024 | | 2024 |
| Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry A Ligeralde, Y Kuang, TE Yerxa, MN Pitcher, M Feller, SY Chung Proceedings of UniReps: the First Workshop on Unifying Representations in …, 2024 | | 2024 |
| Equivariant Self-Supervised Learning Improves IT Predictivity T Yerxa, J Feather, E Simoncelli, SY Chung | | |
