Publications

• ICLR '26 Under review H. Lillemark, B. Huang, F. Zhan, Y. Du, and T. A. Keller (2026). Flow Equivariant World Modeling for Partially Observed Dynamic Environments. In: International Conference on Learning Representations (ICLR). Under review.
• NeurIPS '25 Spotlight
  (Top 13%) T. A. Keller (2025). Flow Equivariant Recurrent Neural Networks. In: Advances in Neural Information Processing Systems (NeurIPS). Spotlight, Top 13% accepted.
• NeurIPS '25 Y. Song, T. A. Keller, S. Brodjian, T. Miyato, Y. Yue, P. Perona, and M. Welling (2025). Kuramoto Orientation Diffusion Models. In: Advances in Neural Information Processing Systems (NeurIPS).
• NeurIPS '25 A. Karuvally, F. Nowak, T. A. Keller, C. A. Alonso, T. Sejnowski, and H. T. Siegelmann (2025). Bridging Expressivity and Scalability with Adaptive Unitary SSMs. Advances in Neural Information Processing Systems (NeurIPS).
• NeurIPS '25 NeurReps
  Workshop S. Bharthulwar, T. A. Keller, M. Theodosis, and D. E. Ba (2025). From Extrapolation to Generalization: How Conditioning Transforms Symmetry Learning in Diffusion Models. In: NeurIPS 2025 Workshop on Symmetry and Geometry in Neural Representations.
• ICLR '26 Under review J. Bertram, L. Dyballa, T. A. Keller, S. Kinger, and S. W. Zucker (2026). How Neural is a Neural Foundation Model? In: International Conference on Learning Representations. Under review. Accepted at Data on Brain & Mind Workshop @ NeurIPS 2025.
• ICLR '26 Under review M. Jacobs, T. Fel, R. Hakim, A. Brondetta, D. E. Ba, and T. A. Keller (2026). Block Recurrent Dynamics in Vision Transformers. In: International Conference on Learning Representations (ICLR). Under review.
• AISTATS '26 E. L. Byrnes Finn, B. Wang, T. A. Keller, and D. E. Ba (2026). Where the Score Lives: A Wavelet View of Diffusion. In: Proceedings of the 29th International Conference on Artificial Intelligence and Statistics (AISTATS). Also accepted at SPIGM Workshop @ NeurIPS 2025.
• CCN '25 Oral (Top 7%) M. Jacobs, R. C. Budzinski, L. Muller, D. E. Ba, and T. A. Keller (2025). Traveling Waves Integrate Spatial Information Through Time. In: Conference on Cognitive Computational Neuroscience (CCN). Oral presentation, Top 7%.
• Springer '25 Book Y. Song, T. A. Keller, N. Sebe, and M. Welling (May 2025). Structured Representation Learning. Synthesis Lectures on Computer Vision. Cham, Switzerland: Springer International Publishing.
• CCN '25 Poster Y. Song, T. A. Keller, Y. Yue, P. Perona, and M. Welling (2025). Langevin Flows for Modeling Neural Latent Dynamics. In: Conference on Cognitive Computational Neuroscience (CCN). arXiv: 2507.11531 [cs.LG].
• ICML '25 HiLD Workshop E. L. B. Finn, T. A. Keller, M. Theodosis, and D. E. Ba (2025). Origins of Creativity in Attention Based Diffusion Models. In: High-dimensional Learning Dynamics 2025 @ ICML ’25.
• COSYNE '25 Abstract T. A. Keller (2025). Nu-Wave State Space Models: Traveling Waves as a Biologically Plausible Context. In: Science Communications Worldwide. doi: 10.57736/b30b-8eed.
• PNAS '25 L. H. B. Liboni, R. C. Budzinski, A. N. Busch, S. Löwe, T. A. Keller, M. Welling, and L. E. Muller (2025). Image segmentation with traveling waves in an exactly solvable recurrent neural network. In: Proceedings of the National Academy of Sciences (PNAS) 122.1, e2321319121. doi: 10.1073/pnas.2321319121.
• ArXiv '24 Preprint E. Finn, T. A. Keller, E. Theodosis, and D. E. Ba (2024). Learning Artistic Signatures: Symmetry Discovery and Style Transfer. arXiv: 2412.04441 [cs.CV].
• Nat. Comms. Under Review T. A. Keller, L. Muller, T. J. Sejnowski, and M. Welling (2024). A Spacetime Perspective on Dynamical Computation in Neural Information Processing Systems. arXiv: 2409.13669 [q-bio.NC].
• TPAMI '24 Journal Y. Song, T. A. Keller, Y. Yue, P. Perona, and M. Welling (2024). Unsupervised Representation Learning from Sparse Transformation Analysis. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. arXiv: 2410.05564 [cs.LG].
• CCN '24 Abstract T. A. Keller, T. Konkle, and C. Conwell (2024). Towards the Use of Relative Representations for Lower-Dimensional, Interpretable Model-to-Brain Mappings. In: Conference on Cognitive Computational Neuroscience (CCN).
• ICLR '24 T. A. Keller, L. Muller, T. Sejnowski, and M. Welling (2024). Traveling Waves Encode the Recent Past and Enhance Sequence Learning. In: International Conference on Learning Representations (ICLR).
• PhD Thesis T. A. Keller. (2023) Natural Inductive Biases for Artificial Intelligence. PhD Thesis. University of Amsterdam
• TMLR '23 T. A. Keller, X. Suau, and L. Zappella (2023). Homomorphic Self-Supervised Learning. In: Transactions on Machine Learning Research. issn: 2835-8856.
• NeurIPS '23 Workshop N. L. Masclef and T. A. Keller (2023). Deep Generative Models of Music Expectation. arXiv: 2310.03500 [cs.SD].
• NeurIPS '23 Y. Song, T. A. Keller, N. Sebe, and M. Welling (2023). Flow Factorized Representation Learning. In: Advances in Neural Information Processing Systems (NeurIPS). vol. 36. Curran Associates, Inc., pp. 49761–49782.
• ICML '23 X. Suau, F. Danieli, T. A. Keller, A. Blaas, C. Huang, J. Ramapuram, D. Busbridge, and L. Zappella (2023). DUET: 2D Structured and Approximately Equivariant Representations. In: Proceedings of the 40th International Conference on Machine Learning. Vol. 202. Proceedings of Machine Learning Research. PMLR, pp. 32749–32769.
• ICML '23 Y. Song, T. A. Keller, N. Sebe, and M. Welling (2023). Latent traversals in generative models as potential flows. In: Proceedings of the 40th International Conference on Machine Learning. ICML’23. Honolulu, Hawaii, USA: JMLR.org.
• ICML '23 T. A. Keller and M. Welling (2023). Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks. In: Proceedings of the 40th International Conference on Machine Learning (ICML). vol. 202. Proceedings of Machine Learning Research, pp. 16168–16189.
• COSYNE '22 Abstract T. A. Keller and M. Welling (2022). Locally Coupled Oscillator Networks Learn Traveling Waves and Topographic Organization.
• SVRHM '22 Best Paper T. A. Keller, Q. Gao, and M. Welling (2021). Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders. In: Shared Visual Representations in Humans and Machines (SVRHM) Workshop @ NeurIPS. Best Paper Award.
• ICCVW '21 Oral T. A. Keller and M. Welling (2021). Predictive Coding with Topographic Variational Autoencoders. In: IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). Oral presentation, pp. 1086–1091. doi: 10.1109/ICCVW54120.2021.00127.
• NeurIPS '21 T. A. Keller and M. Welling (2021). Topographic VAEs Learn Equivariant Capsules. In: Advances in Neural Information Processing Systems (NeurIPS). vol. 34. Curran Associates, Inc., pp. 28585–28597.
• NeurIPS '21 Workshop F. Wever, T. A. Keller, L. Symul, and V. Garcia Satorras (2021). As Easy as APC. In: Self-Supervised Learning Workshop @ NeurIPS.
• ICML '21 T. A. Keller, J. W. T. Peters, P. Jaini, E. Hoogeboom, P. Forré, and M. Welling (July 2021). Self Normalizing Flows. In: Proceedings of the 38th International Conference on Machine Learning (ICML). vol. 139. Proceedings of Machine Learning Research. PMLR, pp. 5378–5387.
• arXiv '18 Preprint T. A. Keller, S. N. Sridhar, and X. Wang (2018). Fast Weight Long Short-Term Memory. In: arXiv preprint. doi: 10.48550/ARXIV.1804.06511.