| Towards Accurate Generative Models of Video: A New Metric & Challenges T Unterthiner*, S van Steenkiste*, K Kurach, R Marinier, M Michalski, ... arXiv preprint arXiv:1812.01717, 2018 | 620 | 2018 |
| Scaling vision transformers to 22 billion parameters M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ... International Conference on Machine Learning, 7480-7512, 2023 | 573 | 2023 |
| Neural Expectation Maximization K Greff*, S van Steenkiste*, J Schmidhuber Advances in Neural Information Processing Systems 30, 6694--6704, 2017 | 334 | 2017 |
| Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions S van Steenkiste, M Chang, K Greff, J Schmidhuber International Conference on Learning Representations, 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 |
| Are Disentangled Representations Helpful for Abstract Visual Reasoning? S van Steenkiste, F Locatello, J Schmidhuber, O Bachem Advances in Neural Information Processing Systems 32, 14222--14235, 2019 | 224 | 2019 |
| FVD: A new Metric for Video Generation T Unterthiner*, S van Steenkiste*, K Kurach, R Marinier, M Michalski, ... ICLR Workshop on Deep Generative Models for Highly Structured Data, 2019 | 156 | 2019 |
| Improving Generalization in Meta Reinforcement Learning using Learned Objectives L Kirsch, S van Steenkiste, J Schmidhuber International Conference on Learning Representations, 2020 | 149 | 2020 |
| Savi++: Towards end-to-end object-centric learning from real-world videos GF Elsayed*, A Mahendran*, S van Steenkiste*, K Greff, MC Mozer, ... Advances in Neural Information Processing Systems, 2022 | 145 | 2022 |
| Object Scene Representation Transformer MSM Sajjadi, D Duckworth*, A Mahendran*, S van Steenkiste*, F Pavetić, ... Advances in Neural Information Processing Systems, 2022 | 110 | 2022 |
| Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks R Csordás, S van Steenkiste, J Schmidhuber International Conference on Learning Representations, 2021 | 107 | 2021 |
| Investigating object compositionality in generative adversarial networks S Van Steenkiste, K Kurach, J Schmidhuber, S Gelly Neural Networks 130, 309-325, 2020 | 61* | 2020 |
| Unsupervised Object Keypoint Learning using Local Spatial Predictability A Gopalakrishnan, S van Steenkiste, J Schmidhuber International Conference on Learning Representations, 2021 | 31 | 2021 |
| Dreamsync: Aligning text-to-image generation with image understanding feedback J Sun, D Fu, Y Hu, S Wang, R Rassin, DC Juan, D Alon, C Herrmann, ... arXiv preprint arXiv:2311.17946, 2023 | 28 | 2023 |
| Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames O Biza, S van Steenkiste, MSM Sajjadi, GF Elsayed, A Mahendran, T Kipf International Conference on Machine Learning, 2023 | 28 | 2023 |
| A Perspective on Objects and Systematic Generalization in Model-Based RL S van Steenkiste*, K Greff*, J Schmidhuber ICML Workshop on Generative Modeling and Model-Based Reasoning for Robotics …, 2019 | 26 | 2019 |
| A Wavelet-based Encoding for Neuroevolution S van Steenkiste, J Koutník, K Driessens, J Schmidhuber Proceedings of the Genetic and Evolutionary Computation Conference 2016, 517-524, 2016 | 23 | 2016 |
| Hierarchical Relational Inference A Stanić, S van Steenkiste, J Schmidhuber Proceedings of the AAAI Conference on Artificial Intelligence 35 (11), 9730 …, 2021 | 22 | 2021 |
| Unsupervised learning of temporal abstractions with slot-based transformers A Gopalakrishnan, K Irie, J Schmidhuber, S van Steenkiste Neural Computation 35 (4), 593-626, 2023 | 17* | 2023 |
| Benchmarking vision language models for cultural understanding S Nayak, K Jain, R Awal, S Reddy, S van Steenkiste, LA Hendricks, ... arXiv preprint arXiv:2407.10920, 2024 | 15 | 2024 |
Juergen SchmidhuberKing Abdullah University of Science and Technology / The Swiss AI Lab, IDSIA / University of LuganoVerificeret mail på kaust.edu.sa
