Anselm Krainovic — accepted AI-conference papers

2025

Improving Deep Learning for Accelerated MRI With Data Filtering

Kang Lin, Anselm Krainovic, Kun Wang, Reinhard Heckel

NeurIPS 2025poster

Deep neural networks achieve state-of-the-art results for accelerated MRI reconstruction. Most research on deep learning based imaging focuses on improving neural network architectures trained and evaluated on fixed and homogeneous training and evaluation data. In this work, we investigate data cura…

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2023

Learning Provably Robust Estimators for Inverse Problems via Jittering

Anselm Krainovic, Mahdi Soltanolkotabi, Reinhard Heckel

NeurIPS 2023poster

Deep neural networks provide excellent performance for inverse problems such as denoising. However, neural networks can be sensitive to adversarial or worst-case perturbations. This raises the question of whether such networks can be trained efficiently to be worst-case robust. In this paper, we inv…

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