Jonas Adler

TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.

TL;DR: The AlphaFold2 dataset as discussed by the authors is a large-scale and high-accuracy structure prediction dataset for protein structures, which is used to evaluate the structural properties of proteins.

TL;DR: The Learned Primal-Dual algorithm for tomographic reconstruction accounts for a (possibly non-linear) forward operator in a deep neural network by unrolling a proximal primal-dual optimization method, but where the proximal operators have been replaced with convolutional neural networks.

TL;DR: In this article, a partially learned approach for the solution of ill-posed inverse problems with not necessarily linear forward operators is proposed, which builds on ideas from classical regularisation theory.

TL;DR: In this article, the learned primal-dual (LPD) algorithm is proposed for tomographic reconstruction, where the proximal operators have been replaced with convolutional neural networks and the algorithm is trained end-to-end, working directly from raw measured data.