| Netsquid, a network simulator for quantum information using discrete events T Coopmans, R Knegjens, A Dahlberg, D Maier, L Nijsten, ... Communications Physics 4 (1), 164, 2021 | 154 | 2021 |
| Event generation and statistical sampling for physics with deep generative models and a density information buffer S Otten, S Caron, W de Swart, M van Beekveld, L Hendriks, ... Nature communications 12 (1), 2985, 2021 | 116 | 2021 |
| Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train V Codreanu, D Podareanu, V Saletore arXiv preprint arXiv:1711.04291, 2017 | 68 | 2017 |
| Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations N Marini, S Marchesin, S Otálora, M Wodzinski, A Caputo, ... NPJ digital medicine 5 (1), 102, 2022 | 42 | 2022 |
| Predicting atmospheric optical properties for radiative transfer computations using neural networks MA Veerman, R Pincus, R Stoffer, CM Van Leeuwen, D Podareanu, ... Philosophical Transactions of the Royal Society A 379 (2194), 20200095, 2021 | 41 | 2021 |
| NetSquid, a discrete-event simulation platform for quantum networks T Coopmans, R Knegjens, A Dahlberg, D Maier, L Nijsten, J Oliveira, ... arXiv e-prints, arXiv: 2010.12535, 2020 | 40 | 2020 |
| Multi_scale_tools: a python library to exploit multi-scale whole slide images N Marini, S Otálora, D Podareanu, M van Rijthoven, J van der Laak, ... Frontiers in Computer Science 3, 684521, 2021 | 21 | 2021 |
| Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flow R Stoffer, CM Van Leeuwen, D Podareanu, V Codreanu, MA Veerman, ... Geoscientific Model Development 14 (6), 3769-3788, 2021 | 18 | 2021 |
| Best practice guide-deep learning D Podareanu, V Codreanu, S Aigner, C Leeuwen, V Weinberg Partnership for Advanced Computing in Europe (PRACE), Tech. Rep 2, 2019 | 12 | 2019 |
| Event generation and statistical sampling for physics with deep generative models and a density information buffer (2019) S Otten, S Caron, W de Swart, M van Beekveld, L Hendriks, ... arXiv preprint arXiv:1901.00875, 1901 | 12 | 1901 |
| Distributed training of generative adversarial networks for fast detector simulation S Vallecorsa, F Carminati, G Khattak, D Podareanu, V Codreanu, ... High Performance Computing: ISC High Performance 2018 International …, 2018 | 11 | 2018 |
| Stainlib: a python library for augmentation and normalization of histopathology H&E images S Otálora, N Marini, D Podareanu, R Hekster, D Tellez, J Van Der Laak, ... BioRxiv, 2022.05. 17.492245, 2022 | 9 | 2022 |
| Caption generation from histopathology whole-slide images using pre-trained transformers BC Guevara, N Marini, S Marchesin, W Aswolinskiy, RJ Schlimbach, ... Medical Imaging with Deep Learning, short paper track, 2023 | 7 | 2023 |
| Large minibatch training on supercomputers with improved accuracy and reduced time to train V Codreanu, D Podareanu, V Saletore 2018 IEEE/ACM Machine Learning in HPC Environments (MLHPC), 67-76, 2018 | 7 | 2018 |
| Less is not more: We need rich datasets to explore L Versluis, M Cetin, C Greeven, K Laursen, D Podareanu, V Codreanu, ... Future Generation Computer Systems 142, 117-130, 2023 | 6 | 2023 |
| Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations. NPJ Digit. Med. 5 (1), 1–18 (2022) N Marini, S Marchesin, S Otlora, M Wodzinski, A Caputo, M Van Rijthoven, ... | 5 | |
| Nature Commun. 12, 2985 (2021) S Otten, S Caron, W de Swart, M van Beekveld, L Hendriks, ... arXiv preprint arXiv:1901.00875, 0 | 5 | |
| Achieving deep learning training in less than 40 minutes on ImageNet-1K & best accuracy and training time on ImageNet-22K & Places-365 with scale-out Intel R Xeon R/Xeon PhiTM … V Codreanu, D Podareanu, V Saletore URL https://blog. surf. nl/en/imagenet-1k-training-on-intel-xeon-phi-in-less …, 0 | 5 | |
| DeepGalaxy: Deducing the properties of galaxy mergers from images using deep neural networks MX Cai, J Bédorf, VA Saletore, V Codreanu, D Podareanu, A Chaibi, ... 2020 IEEE/ACM Fourth Workshop on Deep Learning on Supercomputers (DLS), 56-62, 2020 | 4 | 2020 |
| Densifying assumed-sparse tensors: Improving memory efficiency and mpi collective performance during tensor accumulation for parallelized training of neural machine translation … D Cavdar, V Codreanu, C Karakus, JA Lockman, D Podareanu, ... High Performance Computing: 34th International Conference, ISC High …, 2019 | 4 | 2019 |
