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ORCIDSjoerd van Steenkiste
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2020 – today
- 2022
[i12]Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, Sjoerd van Steenkiste:
Unsupervised Learning of Temporal Abstractions with Slot-based Transformers. CoRR abs/2203.13573 (2022)- 2021
- [j2]Joël M. H. Karel
, Sjoerd van Steenkiste, Ralf L. M. Peeters:
The Design of Matched Balanced Orthogonal Multiwavelets. Frontiers Appl. Math. Stat. 7: 785803 (2021) - [c10]Aleksandar Stanic, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Hierarchical Relational Inference. AAAI 2021: 9730-9738 - [c9]Róbert Csordás, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks. ICLR 2021 - [c8]Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Unsupervised Object Keypoint Learning using Local Spatial Predictability. ICLR 2021 - 2020
- [j1]Sjoerd van Steenkiste, Karol Kurach, Jürgen Schmidhuber, Sylvain Gelly:
Investigating object compositionality in Generative Adversarial Networks. Neural Networks 130: 309-325 (2020) - [c7]Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Improving Generalization in Meta Reinforcement Learning using Learned Objectives. ICLR 2020 - [i11]Róbert Csordás, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks. CoRR abs/2010.02066 (2020) - [i10]Aleksandar Stanic, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Hierarchical Relational Inference. CoRR abs/2010.03635 (2020) - [i9]Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Unsupervised Object Keypoint Learning using Local Spatial Predictability. CoRR abs/2011.12930 (2020) - [i8]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
On the Binding Problem in Artificial Neural Networks. CoRR abs/2012.05208 (2020)
2010 – 2019
- 2019
- [c6]Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly:
FVD: A new Metric for Video Generation. DGS@ICLR 2019 - [c5]Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem:
Are Disentangled Representations Helpful for Abstract Visual Reasoning? NeurIPS 2019: 14222-14235 - [i7]Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem:
Are Disentangled Representations Helpful for Abstract Visual Reasoning? CoRR abs/1905.12506 (2019) - [i6]Sjoerd van Steenkiste, Klaus Greff, Jürgen Schmidhuber:
A Perspective on Objects and Systematic Generalization in Model-Based RL. CoRR abs/1906.01035 (2019) - [i5]Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Improving Generalization in Meta Reinforcement Learning using Learned Objectives. CoRR abs/1910.04098 (2019) - 2018
- [c4]Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber:
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions. ICLR (Poster) 2018 - [i4]Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber:
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions. CoRR abs/1802.10353 (2018) - [i3]Sjoerd van Steenkiste, Karol Kurach, Sylvain Gelly:
A Case for Object Compositionality in Deep Generative Models of Images. CoRR abs/1810.10340 (2018) - [i2]Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly:
Towards Accurate Generative Models of Video: A New Metric & Challenges. CoRR abs/1812.01717 (2018) - 2017
- [c3]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Neural Expectation Maximization. ICLR (Workshop) 2017 - [c2]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Neural Expectation Maximization. NIPS 2017: 6691-6701 - [i1]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Neural Expectation Maximization. CoRR abs/1708.03498 (2017) - 2016
- [c1]Sjoerd van Steenkiste, Jan Koutník, Kurt Driessens, Jürgen Schmidhuber:
A Wavelet-based Encoding for Neuroevolution. GECCO 2016: 517-524
Coauthor Index
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