Inverse problems, compressive sensing, wavelets, deep learning, neural networks.
For more information see my webpage.
- 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]. In the news: [The Register] [Cam. Univ. News] [dotmed].
- N. Gottschling, V. Antun, B. Adcock, A. C. Hansen. The troublesome kernel: why deep learning for inverse problems is typically unstable, [arXiv].
- V. Antun, Ø. Ryan. On the unification of schemes for wavelets on the interval [Preprint] [Technical report]
- L. Thesing, V. Antun, A. C. Hansen. What do AI algorithms actually learn? - On false structures in deep learning, [arXiv].
- B. Adcock, V. Antun, A. C. Hansen. Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling, [arXiv].
Refereed Conference Articles
- 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].
Slides from selected talks
- How intelligent is artificial intelligence ? - On the surprising and mysterious secrets of deep learning (23. May 2019) link.
- Autumn 2019: Lecturing MAT-INF 1100 – Modelling and computations
- Autumn 2016-1018: Plenary exercises in MAT-INF 1100 – Modelling and computations
- Spring 2017: Teaching assistant in MAT-INF 2360 - Applications of linear algebra