A Wavelet-based Encoding for Neuroevolution. - Sjoerd van Steenkiste; Jan Koutník; Kurt Driessens; Jürgen Schmidhuber (GECCO, 2016)

Neural Expectation Maximization. - Klaus Greff; Sjoerd van Steenkiste; Jürgen Schmidhuber (NIPS, 2017)

Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions. - Sjoerd van Steenkiste; Michael Chang; Klaus Greff; Jürgen Schmidhuber (ICLR (Poster), 2018)

A Case for Object Compositionality in Deep Generative Models of Images. - Sjoerd van Steenkiste; Karol Kurach; Sylvain Gelly (CoRR, 2018)

Towards Accurate Generative Models of Video: A New Metric & Challenges. - Thomas Unterthiner; Sjoerd van Steenkiste; Karol Kurach; Raphaël Marinier; Marcin Michalski; Sylvain Gelly (CoRR, 2018)

FVD: A new Metric for Video Generation. - Thomas Unterthiner; Sjoerd van Steenkiste; Karol Kurach; Raphaël Marinier; Marcin Michalski; Sylvain Gelly (DGS@ICLR, 2019)

Are Disentangled Representations Helpful for Abstract Visual Reasoning? - Sjoerd van Steenkiste; Francesco Locatello; Jürgen Schmidhuber; Olivier Bachem (NeurIPS, 2019)

A Perspective on Objects and Systematic Generalization in Model-Based RL. - Sjoerd van Steenkiste; Klaus Greff; Jürgen Schmidhuber (CoRR, 2019)

Improving Generalization in Meta Reinforcement Learning using Learned Objectives. - Louis Kirsch; Sjoerd van Steenkiste; Jürgen Schmidhuber (ICLR, 2020)

On the Binding Problem in Artificial Neural Networks. - Klaus Greff; Sjoerd van Steenkiste; Jürgen Schmidhuber (CoRR, 2020)

Investigating object compositionality in Generative Adversarial Networks. - Sjoerd van Steenkiste; Karol Kurach; Jürgen Schmidhuber; Sylvain Gelly (Neural Networks, 2020)

Hierarchical Relational Inference. - Aleksandar Stanic; Sjoerd van Steenkiste; Jürgen Schmidhuber (AAAI, 2021)

Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks. - Róbert Csordás; Sjoerd van Steenkiste; Jürgen Schmidhuber (ICLR, 2021)

Unsupervised Object Keypoint Learning using Local Spatial Predictability. - Anand Gopalakrishnan; Sjoerd van Steenkiste; Jürgen Schmidhuber (ICLR, 2021)

The Design of Matched Balanced Orthogonal Multiwavelets. - Joël M. H. Karel; Sjoerd van Steenkiste; Ralf L. M. Peeters (Frontiers Appl. Math. Stat., 2021)

SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos. - Gamaleldin F. Elsayed; Aravindh Mahendran; Sjoerd van Steenkiste; Klaus Greff; Michael C. Mozer; Thomas Kipf (NeurIPS, 2022)

Exploring through Random Curiosity with General Value Functions. - Aditya A. Ramesh; Louis Kirsch; Sjoerd van Steenkiste; Jürgen Schmidhuber (NeurIPS, 2022)

Object Scene Representation Transformer. - Mehdi S. M. Sajjadi; Daniel Duckworth; Aravindh Mahendran; Sjoerd van Steenkiste; Filip Pavetic; Mario Lucic; Leonidas J. Guibas; Klaus Greff; Thomas Kipf (NeurIPS, 2022)

Scaling Vision Transformers to 22 Billion Parameters. - Mostafa Dehghani 001; Josip Djolonga; Basil Mustafa; Piotr Padlewski; Jonathan Heek; Justin Gilmer; Andreas Peter Steiner; Mathilde Caron; Robert Geirhos; Ibrahim Alabdulmohsin; Rodolphe Jenatton; Lucas Beyer; et al. (ICML, 2023)

Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames. - Ondrej Biza; Sjoerd van Steenkiste; Mehdi S. M. Sajjadi; Gamaleldin Fathy Elsayed; Aravindh Mahendran; Thomas Kipf (ICML, 2023)

Test-time Adaptation with Slot-Centric Models. - Mihir Prabhudesai; Anirudh Goyal; Sujoy Paul; Sjoerd van Steenkiste; Mehdi S. M. Sajjadi; Gaurav Aggarwal; Thomas Kipf; Deepak Pathak; Katerina Fragkiadaki (ICML, 2023)

Sensitivity of Slot-Based Object-Centric Models to their Number of Slots. - Roland S. Zimmermann; Sjoerd van Steenkiste; Mehdi S. M. Sajjadi; Thomas Kipf; Klaus Greff (CoRR, 2023)

The Impact of Depth and Width on Transformer Language Model Generalization. - Jackson Petty; Sjoerd van Steenkiste; Ishita Dasgupta 001; Fei Sha; Dan Garrette; Tal Linzen (CoRR, 2023)

Unsupervised Learning of Temporal Abstractions With Slot-Based Transformers. - Anand Gopalakrishnan; Kazuki Irie; Jürgen Schmidhuber; Sjoerd van Steenkiste (Neural Comput., 2023)

Benchmarking Vision Language Models for Cultural Understanding. - Shravan Nayak; Kanishk Jain; Rabiul Awal; Siva Reddy; Sjoerd van Steenkiste; Lisa Anne Hendricks; Karolina Stanczak; Aishwarya Agrawal (EMNLP, 2024)

DORSal: Diffusion for Object-centric Representations of Scenes et al. - Allan Jabri; Sjoerd van Steenkiste; Emiel Hoogeboom; Mehdi S. M. Sajjadi; Thomas Kipf (ICLR, 2024)

DyST: Towards Dynamic Neural Scene Representations on Real-World Videos. - Maximilian Seitzer; Sjoerd van Steenkiste; Thomas Kipf; Klaus Greff; Mehdi S. M. Sajjadi (ICLR, 2024)

A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models. - Tiwalayo Eisape; Michael Henry Tessler; Ishita Dasgupta 001; Fei Sha; Sjoerd van Steenkiste; Tal Linzen (NAACL-HLT, 2024)

The Impact of Depth on Compositional Generalization in Transformer Language Models. - Jackson Petty; Sjoerd van Steenkiste; Ishita Dasgupta 001; Fei Sha; Dan Garrette; Tal Linzen (NAACL-HLT, 2024)

Moving Off-the-Grid: Scene-Grounded Video Representations. - Sjoerd van Steenkiste; Daniel Zoran; Yi Yang 007; Yulia Rubanova; Rishabh Kabra; Carl Doersch; Dilara Gokay; Joseph Heyward; Etienne Pot; Klaus Greff; Drew A. Hudson; Thomas Keck; et al. (NeurIPS, 2024)

Neural Assets: 3D-Aware Multi-Object Scene Synthesis with Image Diffusion Models. - Ziyi Wu; Yulia Rubanova; Rishabh Kabra; Drew A. Hudson; Igor Gilitschenski; Yusuf Aytar; Sjoerd van Steenkiste; Kelsey R. Allen; Thomas Kipf (NeurIPS, 2024)

Probabilistic Inverse Cameras: Image to 3D via Multiview Geometry. - Rishabh Kabra; Drew A. Hudson; Sjoerd van Steenkiste; João Carreira 001; Niloy J. Mitra (CoRR, 2024)

Scaling 4D Representations. - João Carreira 001; Dilara Gokay; Michael King; Chuhan Zhang; Ignacio Rocco; Aravindh Mahendran; Thomas Keck; Joseph Heyward; Skanda Koppula; Etienne Pot; Goker Erdogan; Yana Hasson; et al. (CoRR, 2024)

DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback. - Jiao Sun; Deqing Fu; Yushi Hu; Su Wang 001; Royi Rassin; Da-Cheng Juan; Dana Alon; Charles Herrmann; Sjoerd van Steenkiste; Ranjay Krishna; Cyrus Rashtchian (NAACL (Long Papers), 2025)

How Does Code Pretraining Affect Language Model Task Performance? - Jackson Petty; Sjoerd van Steenkiste; Tal Linzen (Trans. Mach. Learn. Res., 2025)

Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models. - Linlu Qiu; Fei Sha; Kelsey R. Allen; Yoon Kim; Tal Linzen; Sjoerd van Steenkiste (CoRR, 2025)

Direct Motion Models for Assessing Generated Videos. - Kelsey R. Allen; Carl Doersch; Guangyao Zhou; Mohammed Suhail; Danny Driess; Ignacio Rocco; Yulia Rubanova; Thomas Kipf; Mehdi S. M. Sajjadi; Kevin Murphy; João Carreira 001; Sjoerd van Steenkiste (CoRR, 2025)

CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics. - Shravan Nayak; Mehar Bhatia; Xiaofeng Zhang; Verena Rieser; Lisa Anne Hendricks; Sjoerd van Steenkiste; Yash Goyal; Karolina Stanczak; Aishwarya Agrawal (CoRR, 2025)