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Results: 154
Number of items: 154
  • Open Access
    Fuchs, F., Worrall, D., Fischer, V., & Welling, M. (2021). SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 3, pp. 1970-1981). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/15231a7ce4ba789d13b722cc5c955834-Abstract.html
  • Open Access
    Bakker, T., Van Hoof, H., & Welling, M. (2021). Experimental design for MRI by greedy policy search. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 23, pp. 18954-18966). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/daed210307f1dbc6f1dd9551408d999f-Abstract.html
  • Open Access
    Blom, T. (2021). Causality and independence in systems of equations. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Weiler, M., Forré, P., Verlinde, E., & Welling, M. (2021). Coordinate Independent Convolutional Networks: Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2106.06020
  • Open Access
    Cohen, T. S. (2021). Equivariant convolutional networks. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Hu, S., Pezzotti, N., & Welling, M. (2021). Learning to Predict Error for MRI Reconstruction. In M. de Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, & C. Essert (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021 : proceedings (Vol. III, pp. 604-613). (Lecture Notes in Computer Science; Vol. 12903). Springer. https://doi.org/10.1007/978-3-030-87199-4_57
  • 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
    Löwe, S., Madras, D., Zemel, R., & Welling, M. (2020). Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. (v2 ed.) ArXiv. https://arxiv.org/abs/2006.10833v1
  • 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
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