Deep networks and the multiple manifold problem S Buchanan, D Gilboa, J Wright ICLR 2021, 2021 | 45 | 2021 |
Efficient dictionary learning with gradient descent D Gilboa, S Buchanan, J Wright International Conference on Machine Learning, 2252-2259, 2019 | 37 | 2019 |
White-box transformers via sparse rate reduction Y Yu, S Buchanan, D Pai, T Chu, Z Wu, S Tong, B Haeffele, Y Ma Advances in Neural Information Processing Systems 36, 9422-9457, 2023 | 34 | 2023 |
Emergence of segmentation with minimalistic white-box transformers Y Yu, T Chu, S Tong, Z Wu, D Pai, S Buchanan, Y Ma CPAL 2024, 2024 | 14 | 2024 |
Deep networks provably classify data on curves T Wang, S Buchanan, D Gilboa, J Wright Advances in neural information processing systems 34, 28940-28953, 2021 | 13 | 2021 |
Canonical factors for hybrid neural fields B Yi, W Zeng, S Buchanan, Y Ma Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 7 | 2023 |
Efficient model-free learning to overcome hardware nonidealities in analog-to-information converters S Buchanan, T Haque, P Kinget, J Wright 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 7 | 2018 |
Resource-Efficient invariant networks: Exponential gains by unrolled optimization S Buchanan, J Yan, E Haber, J Wright arXiv preprint arXiv:2203.05006, 2022 | 5 | 2022 |
Masked completion via structured diffusion with white-box transformers D Pai, S Buchanan, Z Wu, Y Yu, Y Ma The Twelfth International Conference on Learning Representations, 2023 | 2 | 2023 |
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? Y Yu, S Buchanan, D Pai, T Chu, Z Wu, S Tong, H Bai, Y Zhai, ... arXiv preprint arXiv:2311.13110, 2023 | 2 | 2023 |
What's in a Prior? Learned Proximal Networks for Inverse Problems Z Fang, S Buchanan, J Sulam ICLR 2024, 2024 | 1 | 2024 |
Design and fpga implementation of a folded architecture echo state network processor with online training S Buchanan, L Liu | 1 | 2016 |
Deep Networks Through the Lens of Low-Dimensional Structure: Towards Mathematical and Computational Principles for Nonlinear Data S Buchanan Columbia University, 2022 | | 2022 |