Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... nature 596 (7873), 583-589, 2021 | 24074 | 2021 |
Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, ... Nature 596 (7873), 590-596, 2021 | 2136 | 2021 |
Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 913 | 2023 |
Learned primal-dual reconstruction J Adler, O Öktem IEEE transactions on medical imaging, 2018 | 863 | 2018 |
Solving ill-posed inverse problems using iterative deep neural networks J Adler, O Öktem Inverse Problems 33 (12), 124007, 2017 | 693 | 2017 |
Model-based learning for accelerated, limited-view 3-D photoacoustic tomography A Hauptmann, F Lucka, M Betcke, N Huynh, J Adler, B Cox, P Beard, ... IEEE transactions on medical imaging 37 (6), 1382-1393, 2018 | 331 | 2018 |
Banach Wasserstein GAN J Adler, S Lunz Neural Information Processing Systems, 2018 | 302 | 2018 |
Applying and improving AlphaFold at CASP14 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Proteins: Structure, Function, and Bioinformatics 89 (12), 1711-1721, 2021 | 287 | 2021 |
High Accuracy Protein Structure Prediction Using Deep Learning J Jumper, R Evans, A Pritzel, T Green, M Figurnov, K Tunyasuvunakool, ... Fourteenth Critical Assessment of Techniques for Protein Structure Prediction, 2020 | 153 | 2020 |
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 148 | 2024 |
Deep Bayesian Inversion J Adler, O Öktem arXiv preprint arXiv:1811.05910, 2018 | 140 | 2018 |
Accurate structure prediction of biomolecular interactions with AlphaFold 3 J Abramson, J Adler, J Dunger, R Evans, T Green, A Pritzel, ... Nature, 1-3, 2024 | 139 | 2024 |
Operator Discretization Library (ODL) J Adler, H Kohr, O Öktem Software available from https://github. com/odlgroup/odl, 2017 | 134* | 2017 |
Computational predictions of protein structures associated with COVID-19 J Jumper, K Tunyasuvunakool, P Kohli, D Hassabis, the AlphaFold Team DeepMind website, 2020 | 104* | 2020 |
Continuous diffusion for categorical data S Dieleman, L Sartran, A Roshannai, N Savinov, Y Ganin, PH Richemond, ... arXiv preprint arXiv:2211.15089, 2022 | 66 | 2022 |
Multi-scale learned iterative reconstruction A Hauptmann, J Adler, S Arridge, O Öktem IEEE transactions on computational imaging 6, 843-856, 2020 | 49 | 2020 |
Task adapted reconstruction for inverse problems J Adler, S Lunz, O Verdier, CB Schönlieb, O Öktem Inverse Problems 38 (7), 075006, 2022 | 48 | 2022 |
Inferring a continuous distribution of atom coordinates from cryo-EM images using VAEs D Rosenbaum, M Garnelo, M Zielinski, C Beattie, E Clancy, A Huber, ... arXiv preprint arXiv:2106.14108, 2021 | 44 | 2021 |
Learning to solve inverse problems using Wasserstein loss J Adler, A Ringh, O Öktem, J Karlsson NIPS 2017 Optimal Transport and Machine Learning, 2017 | 41 | 2017 |
Data-driven nonsmooth optimization S Banert, A Ringh, J Adler, J Karlsson, O Oktem SIAM Journal on Optimization 30 (1), 102-131, 2020 | 35 | 2020 |