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
121 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
103 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
66 2017 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
37 2020 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
36 2021 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
30 2022 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
17 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 Discussions 2020, 1-29, 2020
16 2020 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 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
8 2019 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
7 2022 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
6 2018 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 Neural Symplectic Integrator with Hamiltonian Inductive Bias for the Gravitational -body Problem MX Cai, SP Zwart, D Podareanu
arXiv preprint arXiv:2111.15631, 2021
3 2021 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
2 2023 Generative Adversarial Networks for Fast Simulation: distributed training and generalisation F Carminati, G Khattak, S Vallecorsa, V Codreanu, D Podareanu, M Cai, ...
Proceedings of Artificial Intelligence for Science, Industry and Society-PoS …, 2020
2 2020