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Results: 8
Number of items: 8
  • Open Access
    Löwe, S. (2024). Learning structured representations of objects and relations. [Thesis, fully internal, Universiteit van Amsterdam].
  • Löwe, S. (2023, September 6). Rotating Features for Object Discovery [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8324835
  • Open Access
    Löwe, S., Lippe, P., Locatello, F., & Welling, M. (2023). Rotating Features for Object Discovery. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems (NeurIPS 2023): 10-16 December 2023, New Orleans, Louisana, USA (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2306.00600
  • 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
    Löwe, S., Lippe, P., Rudolph, M., & Welling, M. (2022). Complex-Valued Autoencoders for Object Discovery. Transactions on Machine Learning Research, 2022, Article 428. https://openreview.net/forum?id=1PfcmFTXoa
  • 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
    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
    Löwe, S., O'Connor, P., & Veeling, B. S. (2020). Putting An End to End-to-End: Gradient-Isolated Learning of Representations. 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. 4, pp. 3016-3028). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2019/hash/851300ee84c2b80ed40f51ed26d866fc-Abstract.html
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