List of Publications
Preprints
- J. S. Wind, V. Antun, A. C. Hansen. Implicit regularization in AI meets generalized hardness of approximation in optimization – Sharp results for diagonal linear networks, 2023.
- N. M. Gottschling, V. Antun, A. C. Hansen, , B. Adcock. The troublesome kernel -- On hallucinations, no free lunches and the accuracy-stability trade-off in inverse problems , 2023.
- N. M. Gottschling, P. Campodonico, V. Antun, A. C. Hansen. On the existence of optimal multi-valued decoders and their accuracy bounds for undersampled inverse problems , 2023.
- L. Thesing, V. Antun and A. C. Hansen. What do AI algorithms actually learn? – On false structures in deep learning, 2019.
Publications
- V. Antun, M. J. Colbrook, A. C. Hansen. Proving Existence Is Not Enough: Mathematical Paradoxes Unravel the Limits of Neural Networks in Artificial Intelligence, SIAM News, 2022, 55(4) 1-4. (Front cover).
- M. J. Colbrook, V. Antun, A. C. Hansen. The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale's 18th problem, Proc. Natl. Acad. Sci., USA, 2022, 119(12), e2107151119. [IEEE Spectrum article about this work]
- M. J. Colbrook, V. Antun, A. C. Hansen. Mathematical paradoxes unearth the boundaries of AI, TheScienceBreaker, 2022, 8(3), 1-2.
- V. Antun. Recovering wavelet coefficients from binary samples using fast transforms, SIAM J. Sci. Comput., 2022, 44(3), A1315-A1336.
- V. Antun, Ø. Ryan. On the unification of schemes and software for wavelets on the interval, Acta Appl. Math., 2021, 173(7), 1-25. [Technical report]
- B. Adcock, V. Antun and A. C. Hansen. Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling. Appl. Comput. Harmon. Anal., 2021, 55, 1-40.
- V. Antun, N. M. Gottschling, A. C. Hansen and B. Adcock. Deep Learning in Scientific Computing: Understanding the Instability Mystery, SIAM News, 2021, 54(2), 3-5.
- V. Antun, F. Renna, C. Poon, B. Adcock and A. C. Hansen. On instabilities of deep learning in image reconstruction and the potential costs of AI, Proc. Natl. Acad. Sci., USA, 2020, 117(48), 30088-30095.
- R. V. Zicari, J. Brusseau, S. N. Blomberg • H. C. Christensen, M. Coffee, M. B. Ganapini, S. Gerke, T. K. Gilbert, E. Hickman, E. Hildt, S. Holm, U. Kühne, V. I. Madai, W. Osika, A. Spezzatti, E. Schnebel, J. J. Tithi, D. Vetter, M. Westerlund, R. Wurth. J. Amann, V. Antun, V. Beretta, F. Bruneault, E. Campano, B. Düdder, A. Gallucci, E. Goffi, C. B. Haase, T. Hagendorff, P. Kringen, F. Möslein, D. Ottenheimer, M. Ozols, L. Palazzani, M. Petrin, K. Tafur, J. Tørresen, H. Volland, G. Kararigas. On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls, Front. Hum. Dyn., 08 July 2021.
Refereed Conference Articles
- M. J. Colbrook, V. Antun, A. C. Hansen. On the existence of stable and accurate neural networks for image reconstruction, Signal Processing with Adaptive Sparse Structured Representations (SPARS), 2019.