| On the stable sampling rate for binary measurements and wavelet reconstruction AC Hansen, L Thesing Applied and Computational Harmonic Analysis 48 (2), 630-654, 2020 | 13 | 2020 |
| What do AI algorithms actually learn?-On false structures in deep learning L Thesing, V Antun, AC Hansen arXiv preprint arXiv:1906.01478, 2019 | 11 | 2019 |
| Linear reconstructions and the analysis of the stable sampling rate L Thesing, AC Hansen Sampling Theory in Signal and Image Processing, 103-126, 2018 | 8 | 2018 |
| Sumformer: Universal approximation for efficient transformers S Alberti, N Dern, L Thesing, G Kutyniok Topological, Algebraic and Geometric Learning Workshops 2023, 72-86, 2023 | 7 | 2023 |
| Non-uniform recovery guarantees for binary measurements and infinite-dimensional compressed sensing L Thesing, AC Hansen Journal of Fourier Analysis and Applications 27 (2), 14, 2021 | 5 | 2021 |
| On reconstructions from measurements with binary functions R Calderbank, A Hansen, B Roman, L Thesing Springer, to appear, 0 | 2* | |
| Which neural networks can be computed by an algorithm?–Generalised hardness of approximation meets Deep Learning L Thesing, AC Hansen PAMM 22 (1), e202200174, 2023 | | 2023 |
| On the Foundations of Computation and Sampling for Reconstruction and Approximation L Thesing | | 2022 |
| A stable learning framework L Thesing | | 2019 |
| The Oracle of DLphi W Baines, J Blechschmidt, MJ del Razo Sarmina, A Drory, D Elbrächter, ... arXiv preprint arXiv:1901.05744, 2019 | | 2019 |
