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Keller, T. A., & Welling, M. (2022). Topographic VAEs learn Equivariant Capsules. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6-14 December 2021 (Vol. 34, pp. 28585-28597). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2109.01394[details]
Keller, T. A., & Welling, M. (2021). Predictive Coding with Topographic Variational Autoencoders. In 2021 IEEE/CVF International Conference on Computer Vision Workshops: proceedings : ICCVW 2021 : 11-17 October 2021, virtual event (pp. 1086-1091). IEEE Computer Society. https://doi.org/10.1109/ICCVW54120.2021.00127[details]
Keller, T. A., Peters, J. W. T., Jaini, P., Hoogeboom, E., Forré, P., & Welling, M. (2021). Self Normalizing Flows. Proceedings of Machine Learning Research, 139, 5378-5387. https://arxiv.org/abs/2011.07248[details]
Keller, T. A., Gao, Q., & Welling, M. (2021). Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders. Paper presented at 3rd Workshop on Shared Visual Representations in Human and Machine Intelligence of the Neural Information Processing Systems conference. https://arxiv.org/abs/2110.13911
Wever, F., Keller, T. A., García Satorras, V., & Symul, L. (2021). As easy as APC: Leveraging self-supervised learning in the context of time series classification with varying levels of sparsity and severe class imbalance.. Paper presented at 2nd Workshop on Self-Supervised Learning: Theory and Practice of the 35th Conference on Neural Information Processing Systems. https://arxiv.org/abs/2106.15577
2023
Keller, T. A. (2023). Natural inductive biases for artificial intelligence. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
Liboni, L., Budzinski, R., Busch, A., Löwe, S., Keller, T. A., Welling, M., & Muller, L. (2023). Image segmentation with traveling waves in an exactly solvable recurrent neural network.
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