Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 8
Number of items: 8
  • Open Access
    Zhdanov, M., Ruhe, D., Weiler, M., Lucic, A., Forré, P. D., Brandstetter, J., & Forré, P. (2024). Clifford-steerable convolutional neural networks. Proceedings of Machine Learning Research, 235, 61203-612228. https://proceedings.mlr.press/v235/zhdanov24a.html
  • Open Access
    Weiler, M. (2024). Equivariant and coordinate independent convolutional networks: A gauge field theory of neural networks. [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., Geiger, M., & Weiler, M. (2020). A General Theory of Equivariant CNNs on Homogeneous Spaces. 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. 12, pp. 9113-9124). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/book/advances-in-neural-information-processing-systems-32-2019
  • Open Access
    Weiler, M., & Cesa, G. (2020). General E(2)-Equivariant Steerable CNNs. 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. 19, pp. 14290-14301). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2019/hash/45d6637b718d0f24a237069fe41b0db4-Abstract.html
  • Open Access
    Weiler, M., Boomsma, W., Geiger, M., Welling, M., & Cohen, T. (2019). 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems 2018 : Montreal, Canada, 3-8 December 2018 (Vol. 15, pp. 10381-10392). (Advances in Neural Information Processing Systems; Vol. 31). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2018/hash/488e4104520c6aab692863cc1dba45af-Abstract.html
  • Open Access
    Cohen, T. S., Weiler, M., Kicanaoglu, B., & Welling, M. (2019). Gauge Equivariant Convolutional Networks and the Icosahedral CNN. Proceedings of Machine Learning Research, 97, 1321-1330. http://proceedings.mlr.press/v97/cohen19d.html
  • Open Access
    Falorsi, L., de Haan, P., Davidson, T. R., De Cao, N., Weiler, M., Forré, P., & Cohen, T. S. (2018). Explorations in Homeomorphic Variational Auto-Encoding. Paper presented at ICML18 Workshop on Theoretical Foundations and Applications
    of Deep Generative Models, Stockholm, Sweden. https://arxiv.org/abs/1807.04689
Page of