| Laion-400m: Open dataset of clip-filtered 400 million image-text pairs C Schuhmann, R Vencu, R Beaumont, R Kaczmarczyk, C Mullis, A Katta, ... arXiv preprint arXiv:2111.02114, 2021 | 274 | 2021 |
| Laion-5b: An open large-scale dataset for training next generation image-text models C Schuhmann, R Beaumont, R Vencu, C Gordon, R Wightman, M Cherti, ... arXiv preprint arXiv:2210.08402, 2022 | 255 | 2022 |
| Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows M Bode, M Gauding, Z Lian, D Denker, M Davidovic, K Kleinheinz, J Jitsev, ... Proceedings of the Combustion Institute 38 (2), 2617-2625, 2021 | 63 | 2021 |
| Hierarchical information-based clustering for connectivity-based cortex parcellation NS Gorbach, C Schütte, C Melzer, M Goldau, O Sujazow, J Jitsev, ... Frontiers in neuroinformatics 5, 18, 2011 | 45 | 2011 |
| Reproducible scaling laws for contrastive language-image learning M Cherti, R Beaumont, R Wightman, M Wortsman, G Ilharco, C Gordon, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 29 | 2023 |
| Remote sensing big data classification with high performance distributed deep learning R Sedona, G Cavallaro, J Jitsev, A Strube, M Riedel, JA Benediktsson Remote Sensing 11 (24), 3056, 2019 | 29 | 2019 |
| Using physics-informed super-resolution generative adversarial networks for subgrid modeling in turbulent reactive flows M Bode, M Gauding, Z Lian, D Denker, M Davidovic, K Kleinheinz, J Jitsev, ... arXiv preprint arXiv:1911.11380, 2019 | 23 | 2019 |
| Adversarial domain adaptation to reduce sample bias of a high energy physics event classifier JM Clavijo, P Glaysher, J Jitsev, JM Katzy Machine learning: science and technology 3 (1), 015014, 2021 | 19 | 2021 |
| Experience-driven formation of parts-based representations in a model of layered visual memory J Jitsev, C V Der Malsburg Frontiers in computational neuroscience, 15, 2009 | 17 | 2009 |
| Effect of pre-training scale on intra-and inter-domain, full and few-shot transfer learning for natural and X-ray chest images M Cherti, J Jitsev 2022 International Joint Conference on Neural Networks (IJCNN), 1-9, 2022 | 13* | 2022 |
| Towards prediction of turbulent flows at high reynolds numbers using high performance computing data and deep learning M Bode, M Gauding, JH Göbbert, B Liao, J Jitsev, H Pitsch High Performance Computing: ISC High Performance 2018 International …, 2019 | 13 | 2019 |
| Corticostriatal circuit mechanisms of value-based action selection: Implementation of reinforcement learning algorithms and beyond K Morita, J Jitsev, A Morrison Behavioural brain research 311, 110-121, 2016 | 12 | 2016 |
| Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells A Colliard-Granero, M Batool, J Jankovic, J Jitsev, MH Eikerling, K Malek, ... Nanoscale 14 (1), 10-18, 2022 | 11 | 2022 |
| Super-resolution of large volumes of Sentinel-2 images with high performance distributed deep learning R Zhang, G Cavallaro, J Jitsev IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium …, 2020 | 11 | 2020 |
| Learning from positive and negative rewards in a spiking neural network model of basal ganglia J Jitsev, A Morrison, M Tittgemeyer The 2012 International Joint Conference on Neural Networks (IJCNN), 1-8, 2012 | 11 | 2012 |
| Off-line memory reprocessing following on-line unsupervised learning strongly improves recognition performance in a hierarchical visual memory J Jitsev, C von der Malsburg The 2010 International Joint Conference on Neural Networks (IJCNN), 1-8, 2010 | 9 | 2010 |
| Information-theoretic connectivity-based cortex parcellation NS Gorbach, S Siep, J Jitsev, C Melzer, M Tittgemeyer Machine Learning and Interpretation in Neuroimaging: International Workshop …, 2012 | 8 | 2012 |
| On the self-organization of a hierarchical memory for compositional object representation in the visual cortex E Jitsev Frankfurt (Main), Univ., Diss., 2010, 2010 | 8 | 2010 |
| A visual object recognition system invariant to scale and rotation YD Sato, J Jitsev, C Von Der Malsburg Artificial Neural Networks-ICANN 2008: 18th International Conference, Prague …, 2008 | 8 | 2008 |
| Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells MJ Eslamibidgoli, FP Tipp, J Jitsev, J Jankovic, MH Eikerling, K Malek RSC advances 11 (51), 32126-32134, 2021 | 7 | 2021 |
Mehdi ChertiPostdoc at Forschungszentrum JülichVerified email at fz-juelich.de
