List of Publications

Preprints

  1. 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.
  2. 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.
  3. 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.
  4. L. Thesing, V. Antun and A. C. Hansen. What do AI algorithms actually learn? – On false structures in deep learning, 2019.

Publications

  1. 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).
  2. 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]
  3. M. J. Colbrook, V. Antun, A. C. Hansen. Mathematical paradoxes unearth the boundaries of AI, TheScienceBreaker, 2022, 8(3), 1-2.
  4. V. Antun. Recovering wavelet coefficients from binary samples using fast transforms, SIAM J. Sci. Comput., 2022, 44(3), A1315-A1336.
  5. 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]
  6. 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.
  7. 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.
  8. 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.
  9. 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

  1. 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.

PhD Thesis

V. Antun. Stability and accuracy in compressive sensing and deep learning, University of Oslo, 2020.

Master Thesis

V. Antun. Coherence estimates between Hadamard matrices and Daubechies wavelets, University of Oslo, 2016.