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Results: 160
Number of items: 160
  • Open Access
    Garcia Satorras, V., Hoogeboom, E., & Welling, M. (2021). E(n) Equivariant Graph Neural Networks. Proceedings of Machine Learning Research, 139, 9323-9332. https://proceedings.mlr.press/v139/satorras21a.html
  • Open Access
    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
  • Open Access
    Shang, W. (2021). Crafting deep learning models for reinforcement learning and computer vision applications. [Thesis, fully internal, Universiteit van Amsterdam].
  • van der Pol, E., Kipf, T., Oliehoek, F. A., & Welling, M. (2020). Plannable Approximations to MDP Homomorphisms: Equivariance under Actions. In AAMAS'20: proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems : May 9-13, 2020, Auckland, New Zealand (pp. 1431–1439). International Foundation for Autonomous Agents and Multiagent Systems. https://dl.acm.org/doi/10.5555/3398761.3398926
  • Open Access
    Shang, W., van der Wal, D., van Hoof, H., & Welling, M. (2020). Stochastic Activation Actor Critic Methods. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019 : proceedings (Vol. III, pp. 103-117). (Lecture Notes in Computer Science; Vol. 11908), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-46133-1_7
  • Open Access
    Worrall, D., & Welling, M. (2020). Deep Scale-spaces: Equivariance Over Scale. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada, 8-14 December 2019 (Vol. 10, pp. 7334-7346). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2019/hash/f04cd7399b2b0128970efb6d20b5c551-Abstract.html
  • Open Access
    O'Connor, P. (2020). Biologically plausible deep learning: Should airplanes flap their wings? [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Hoogeboom, E., Peters, J. W. T., van den Berg, R., & Welling, M. (2020). Integer Discrete Flows and Lossless Compression. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada, 8-14 December 2019 (Vol. 16, pp. 12114-12124). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2019/hash/9e9a30b74c49d07d8150c8c83b1ccf07-Abstract.html
  • Open Access
    Kool, W., van Hoof, H., & Welling, M. (2020). Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement. Journal of Machine Learning Research, 21, Article 47. https://jmlr.csail.mit.edu/papers/v21/19-985.html
  • Open Access
    Ullrich, K. (2020). A coding perspective on deep latent variable models. [Thesis, fully internal, Universiteit van Amsterdam].
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