| LSTM: A search space odyssey K Greff, RK Srivastava, J Koutník, BR Steunebrink, J Schmidhuber IEEE transactions on neural networks and learning systems 28 (10), 2222-2232, 2016 | 7951 | 2016 |
| Training very deep networks RK Srivastava, K Greff, J Schmidhuber Advances in neural information processing systems 28, 2015 | 3477 | 2015 |
| Highway networks RK Srivastava, K Greff, J Schmidhuber arXiv preprint arXiv:1505.00387, 2015 | 2734 | 2015 |
| Palm-e: An embodied multimodal language model D Driess, F Xia, MSM Sajjadi, C Lynch, A Chowdhery, B Ichter, A Wahid, ... arXiv preprint arXiv:2303.03378, 2023 | 1608 | 2023 |
| A Clockwork RNN J Koutnik, K Greff, F Gomez, J Schmidhuber International Conference on Machine Learning, 1863-1871, 2014 | 713 | 2014 |
| Multi-Object Representation Learning with Iterative Variational Inference K Greff, RL Kaufman, R Kabra, N Watters, C Burgess, D Zoran, L Matthey, ... arXiv preprint arXiv:1903.00450, 2019 | 540 | 2019 |
| Neural Expectation Maximization K Greff, S van Steenkiste, J Schmidhuber Advances in Neural Information Processing Systems 30, 6694--6704, 2017 | 329 | 2017 |
| Relational neural expectation maximization: Unsupervised discovery of objects and their interactions S Van Steenkiste, M Chang, K Greff, J Schmidhuber arXiv preprint arXiv:1802.10353, 2018 | 321 | 2018 |
| On the binding problem in artificial neural networks K Greff, S Van Steenkiste, J Schmidhuber arXiv preprint arXiv:2012.05208, 2020 | 298 | 2020 |
| Highway and residual networks learn unrolled iterative estimation K Greff, RK Srivastava, J Schmidhuber arXiv preprint arXiv:1612.07771, 2016 | 279 | 2016 |
| Conditional object-centric learning from video T Kipf, GF Elsayed, A Mahendran, A Stone, S Sabour, G Heigold, ... arXiv preprint arXiv:2111.12594, 2021 | 219 | 2021 |
| Kubric: A scalable dataset generator K Greff, F Belletti, L Beyer, C Doersch, Y Du, D Duckworth, DJ Fleet, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 214 | 2022 |
| Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters J Luketina, M Berglund, K Greff, T Raiko arXiv preprint, 2015 | 208 | 2015 |
| Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations MSM Sajjadi, H Meyer, E Pot, U Bergmann, K Greff, N Radwan, S Vora, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 187 | 2022 |
| Tagger: Deep unsupervised perceptual grouping K Greff, A Rasmus, M Berglund, T Hao, H Valpola, J Schmidhuber Advances in Neural Information Processing Systems 29, 2016 | 177 | 2016 |
| Savi++: Towards end-to-end object-centric learning from real-world videos G Elsayed, A Mahendran, S Van Steenkiste, K Greff, MC Mozer, T Kipf Advances in Neural Information Processing Systems 35, 28940-28954, 2022 | 145 | 2022 |
| Object scene representation transformer MSM Sajjadi, D Duckworth, A Mahendran, S Van Steenkiste, F Pavetic, ... Advances in neural information processing systems 35, 9512-9524, 2022 | 108 | 2022 |
| The Sacred Infrastructure for Computational Research. K Greff, A Klein, M Chovanec, F Hutter, J Schmidhuber SciPy 17, 49-56, 2017 | 100 | 2017 |
| Slotformer: Unsupervised visual dynamics simulation with object-centric models Z Wu, N Dvornik, K Greff, T Kipf, A Garg arXiv preprint arXiv:2210.05861, 2022 | 91 | 2022 |
| Highway networks. arXiv 2015 RK Srivastava, K Greff, J Schmidhuber arXiv preprint arXiv:1505.00387, 2015 | 86 | 2015 |
Juergen SchmidhuberKing Abdullah University of Science and Technology / The Swiss AI Lab, IDSIA / University of LuganoVerified email at kaust.edu.sa