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Results: 156
Number of items: 156
  • Open Access
    Cohen, T. S. (2021). Equivariant convolutional networks. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Shang, W. (2021). Crafting deep learning models for reinforcement learning and computer vision applications. [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
    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
    Nielsen, D., Jaini, P., Hoogeboom, E., Winther, O., & Welling, M. (2021). SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. 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. 16, pp. 12685-12696). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/9578a63fbe545bd82cc5bbe749636af1-Abstract.html
  • Open Access
    Ottenhoff, M. C., Ramos, L. A., Potters, W., Hu, S., Thomas, R., Elbers, P., Welling, M., Simsek, S., Wiersinga, W. J., van Wingen, G. A., & The Dutch COVID-PREDICT research group (2021). Predicting mortality of individual patients with COVID-19: a multicentre Dutch cohort. BMJ Open, 11(7), Article e047347. https://doi.org/10.1136/bmjopen-2020-047347
  • 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
    Hu, S., Fridgeirsson, E. A., van Wingen, G., & Welling, M. (2021). Transformer-Based Deep Survival Analysis. Proceedings of Machine Learning Research, 146, 132-148. https://proceedings.mlr.press/v146/hu21a.html
  • 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
  • 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
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