Vegard Antun

According to our database, Vegard Antun authored at least 9 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Achieving Data Efficient Neural Networks with Hybrid Concept-based Models.

Tobias A. Opsahl, Vegard Antun

CoRR, 2024

2023

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

2022

Recovering Wavelet Coefficients from Binary Samples Using Fast Transforms.

Vegard Antun

SIAM J. Sci. Comput., 2022

2021

Stability and accuracy in compressive sensing and deep learning.

Vegard Antun

PhD thesis, 2021

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

2020

The troublesome kernel: why deep learning for inverse problems is typically unstable.

Nina M. Gottschling, Vegard Antun, Ben Adcock, Anders C. Hansen

CoRR, 2020

2019

What do AI algorithms actually learn? - On false structures in deep learning.

Laura Thesing, Vegard Antun, 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

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