S. (Sindy) Löwe MSc
Faculty of Science
Informatics Institute
Visiting address
- Science Park 904
Postal address
- Postbus 94323
1090 GH Amsterdam
Contact details
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Publications
2022
- Lippe, P., Magliacane, S., Löwe, S., Asano, Y. M., Cohen, T., & Gavves, S. (2022). CITRIS: Causal Identifiability from Temporal Intervened Sequences. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, & S. Sabato (Eds.), Proceedings of the 39th International Conference on Machine Learning (Vol. 162, pp. 13557-13603). (Proceedings of Machine Learning Research). PMLR. https://proceedings.mlr.press/v162/lippe22a.html
Löwe, S., Lippe, P., Rudolph, M., & Welling, M. (2022). Complex-Valued Autoencoders for Object Discovery. Transactions on Machine Learning Research, [428]. https://openreview.net/forum?id=1PfcmFTXoa
2020
- 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 [details]
2021
- de Haan, P., & Löwe, S. (2021). Contrastive Predictive Coding for Anomaly Detection. Paper presented at ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning. https://doi.org/10.48550/arXiv.2107.07820
2020
- Löwe, S., Greff, K., Jonschkowski, R., Dosovitskiy, A., & Kipf, T. (2020). Learning Object-Centric Video Models by Contrasting Sets. Paper presented at NeurIPS 2020 Workshop on Object Representations for Learning and Reasoning. https://arxiv.org/abs/2011.10287
Others
- Löwe, S. (participant), Choromanska, A. (organiser), Gori, M. (organiser), Huang, Y. (organiser), Malinowski, M. (organiser), Patraucean, V. (organiser) & Swirszcz, G. (organiser) (12-12-2020). Beyond Backpropagation - Novel Ideas for Training Neural Architectures (organising a conference, workshop, ...). https://beyondbackprop.github.io/
2020
- Löwe, S., Madras, D., Zemel, R., & Welling, M. (2020). Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. (v2 ed.) arXiv.org. https://arxiv.org/abs/2006.10833v1 [details]
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Ancillary activities
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Google
research internship
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Google
Löwe, S., Lippe, P., Rudolph, M., & Welling, M. (2022). Complex-Valued Autoencoders for Object Discovery. Transactions on Machine Learning Research, [428].