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Results: 19
Number of items: 19
  • Open Access
    Lippe, P. (2025). Learning causal representations in spatio-temporal systems. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    de Haan, P. (2025). Machine learning with generalised symmetries. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Lippe, P., Magliacane, S., Löwe, S., Asano, Y. M., Cohen, T., & Gavves, E. (2023). BISCUIT: Causal Representation Learning from Binary Interactions. Proceedings of Machine Learning Research, 216, 1263-1273. https://proceedings.mlr.press/v216/lippe23a.html
  • Open Access
    Brehmer, J., Cohen, T., De Haan, P., & Lippe, P. (2023). Weakly supervised causal representation learning. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 50, pp. 38319-38331). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2203.16437
  • Open Access
    Lippe, P., Magliacane, S., Löwe, S., Asano, Y. M., Cohen, T., & Gavves, E. (2022). CITRIS: Causal Identifiability from Temporal Intervened Sequences. Proceedings of Machine Learning Research, 162, 13557-13603. https://proceedings.mlr.press/v162/lippe22a.html
  • Open Access
    De Haan, P., Cohen, T. S., & Welling, M. (2021). Natural Graph 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. 5, pp. 3636-3646). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/2517756c5a9be6ac007fe9bb7fb92611-Abstract.html
  • Open Access
    Cohen, T. S. (2021). Equivariant convolutional networks. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Pervez, A., Cohen, T., & Gavves, E. (2020). Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks. Proceedings of Machine Learning Research, 119, 7632-7640. http://proceedings.mlr.press/v119/pervez20a.html
  • 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., 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
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