On instabilities of deep learning in image reconstruction - Does AI come at a cost? - Vegard Antun; Francesco Renna; Clarice Poon; Ben Adcock; Anders C. Hansen (CoRR, 2019)

Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling. - Ben Adcock; Vegard Antun; Anders C. Hansen (CoRR, 2019)

What do AI algorithms actually learn? - On false structures in deep learning. - Laura Thesing; Vegard Antun; Anders C. Hansen (CoRR, 2019)

The troublesome kernel: why deep learning for inverse problems is typically unstable. - Nina Maria Gottschling; Vegard Antun; Ben Adcock; Anders C. Hansen (CoRR, 2020)

Can stable and accurate neural networks be computed? - On the barriers of deep learning and Smale's 18th problem. - Vegard Antun; Matthew J. Colbrook; Anders C. Hansen (CoRR, 2021)

Stability and accuracy in compressive sensing and deep learning. - Vegard Antun (2021)

Recovering Wavelet Coefficients from Binary Samples Using Fast Transforms. - Vegard Antun (SIAM J. Sci. Comput., 2022)

Implicit regularization in AI meets generalized hardness of approximation in optimization - Sharp results for diagonal linear networks. - Johan S. Wind; Vegard Antun; Anders C. Hansen (CoRR, 2023)

Achieving Data Efficient Neural Networks with Hybrid Concept-based Models. - Tobias A. Opsahl; Vegard Antun (CoRR, 2024)

The Troublesome Kernel: On Hallucinations, No Free Lunches, and the Accuracy-Stability Tradeoff in Inverse Problems. - Nina Maria Gottschling; Vegard Antun; Anders C. Hansen; Ben Adcock (SIAM Rev., 2025)