Vegard Antun

Bilde av Vegard Antun
English version of this page
Mobiltelefon 91553728
Rom 1005
Brukernavn
Besøksadresse Moltke Moes vei 35 Niels Henrik Abels hus 0851 OSLO
Postadresse Postboks 1053 Blindern 0316 Oslo

Faglige interesser

Inverse problemer, compressive sensing, wavelets, deep learning, nevrale nettverk.

For mer informasjon se websiden min.

Publikasjoner

  • V. Antun, M. J. Colbrook, A. C. Hansen. Proving Existence Is Not Enough: Mathematical Paradoxes Unravel the Limits of Neural Networks in Artificial IntelligenceSIAM News, 2022, 55 (4) 1-4.
  • M. 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 problemProc. Natl. Acad. Sci., USA, 2022, 119(12)  e2107151119.
  • V. Antun. Recovering wavelet coefficients from binary samples using fast transforms, SIAM J. Sci. Comput., 2022, 44(3), A1315-A1336. [arXiv].
  • V. Antun, Ø. Ryan. On the unification of schemes and software for wavelets on the intervalActa Appl. Math., 2021. [Technical report].
  • B. Adcock, V. Antun, A. C. Hansen. Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling. Appl. Comput. Harmon. Anal., 2021 (to appear). [arXiv].
  • V. Antun, N. M. Gottschling, A. C. Hansen, B. Adcock. Deep Learning in Scientific Computing: Understanding the Instability Mystery, SIAM News, 2021.
  • V. Antun, F. Renna, C. Poon, B. Adcock, A. C. Hansen. On instabilities of deep learning in image reconstruction and the potential costs of AI. Proc. Natl. Acad. Sci. USA, 2020. [arXiv]. I nyhetene: [titan.uio.no][The Register] [Cam. Univ. News] [dotmed].
  • 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 CallsFront. Hum. Dyn., 08 July 2021.

Preprints

  • N. Gottschling, V. Antun, B. Adcock, A. C. Hansen. The troublesome kernel: why deep learning for inverse problems is typically unstable, [arXiv].
  • L. Thesing, V. Antun, A. C. Hansen. What do AI algorithms actually learn? - On false structures in deep learning, [arXiv].

Fagfellevurderte konferanseartikler

  • M. 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, [PDF].

PhD avhandling

  • V. Antun. Stability and accuracy in compressive sensing and deep learning [PDF].

Undervisning

Vår 2022: Foreleser MIEVU4020 – Introduksjon til dyp læring

Høst 2020: Gruppelærer/Plenumsregner i MAT3110 - Innføring i numerisk analyse

Høst 2019: Foreleser i MAT-INF 1100 – Modellering og beregninger

Høst 2016-2018: Plenumsregner i  MAT-INF 1100 – Modellering og beregninger

Vår 2017: Gruppelærer i MAT-INF 2360 - Anvendelser av lineær algebra

 

Emneord: Matematikk, Beregningsorientert matematikk
Publisert 22. aug. 2016 16:25 - Sist endret 20. mai 2022 16:57