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83 Publications


2024 | Published | Conference Paper | IST-REx-ID: 18847 | OA
Cadei, R., Lindorfer, L., Cremer, S., Schmid, C., & Locatello, F. (2024). Smoke and mirrors in causal downstream tasks. In ICML 2024 Workshop AI4Science (Vol. 38). Curran Associates.
[Published Version] View | Files available | arXiv
 

2024 | Research Data Reference | IST-REx-ID: 18895 | OA
Cadei, R., Locatello, F., Cremer, S., Lindorfer, L., & Schmid, C. (2024). ISTAnt. Institute of Science and Technology Austria. https://doi.org/10.6084/M9.FIGSHARE.26484934.V2
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2024 | Published | Conference Paper | IST-REx-ID: 18956 | OA
Demirel, B., & Ozkan, H. (2024). Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image. In 2024 IEEE International Conference on Image Processing (pp. 977–983). Abu Dhabi, United Arab Emirates: IEEE. https://doi.org/10.1109/icip51287.2024.10647499
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18964 | OA
Fan, K., Bai, Z., Xiao, T., He, T., Horn, M., Fu, Y., … Zhang, Z. (2024). Adaptive slot attention: Object discovery with dynamic slot number. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, United States: IEEE. https://doi.org/10.1109/cvpr52733.2024.02176
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18971 | OA
Arefin, R., Zhang, Y., Baratin, A., Locatello, F., Rish, I., Liu, D., & Kawaguchi, K. (2024). Unsupervised concept discovery mitigates spurious correlations. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 1672–1688). Vienna, Austria: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18996 | OA
Chen, T., Bello, K., Locatello, F., Aragam, B., & Ravikumar, P. K. (2024). Identifying general mechanism shifts in linear causal representations. In 38th Conference on Neural Information Processing Systems (Vol. 38). Vancouver, Canada: Curran Associates.
[Published Version] View | Files available | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 19010 | OA
Yao, D., Rancati, D., Cadei, R., Fumero, M., & Locatello, F. (2024). Unifying causal representation learning with the invariance principle. In 38th Conference on Neural Information Processing Systems (Vol. 38). Vancouver, Canada: Curran Associates.
[Published Version] View | Files available | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 19005 | OA
Yao, D., Muller, C. J., & Locatello, F. (2024). Marrying causal representation learning with dynamical systems for science. In 38th Conference on Neural Information Processing Systems (Vol. 38). Vancouver, Canada: Curran Associates.
[Published Version] View | Files available | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 19007 | OA
Kori, A., Locatello, F., Santhirasekaram, A., Toni, F., Glocker, B., & De Sousa Ribeiro, F. (2024). Identifiable object-centric representation learning via probabilistic slot attention. In 38th Conference on Neural Information Processing Systems (Vol. 38). Vancouver, Canada: Curran Associates.
[Published Version] View | Files available | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 14946 | OA
Yao, D., Xu, D., Lachapelle, S., Magliacane, S., Taslakian, P., Martius, G., … Locatello, F. (2024). Multi-view causal representation learning with partial observability. In 12th International Conference on Learning Representations. Vienna, Austria: Curran Associates.
[Published Version] View | Files available | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 14213 | OA
Lao, D., Hu, Z., Locatello, F., Yang, Y., & Soatto, S. (2024). Divided attention: Unsupervised multi-object discovery with contextually separated slots. In 1st Conference on Parsimony and Learning. Hong Kong, China.
[Published Version] View | Files available | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18114 | OA
Pervez, A. A., Locatello, F., & Gavves, E. (2024). Mechanistic neural networks for scientific machine learning. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 40484–40501). Vienna, Austria: ML Research Press.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

2023 | Patent | IST-REx-ID: 14965 | OA
Ficek, J., Lehmann, K.-V., Locatello, F., Raetsch, G., & Stark, S. (2023). Methods of determining correspondences between biological properties of cells.
[Published Version] View | Files available
 

2023 | Published | Conference Paper | IST-REx-ID: 14216 | OA
Norelli, A., Fumero, M., Maiorca, V., Moschella, L., Rodolà, E., & Locatello, F. (2023). ASIF: Coupled data turns unimodal models to multimodal without training. In 37th Conference on Neural Information Processing Systems (Vol. 36, pp. 15303–15319). New Orleans, LA, United States: Curran Associates.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2023 | Published | Conference Paper | IST-REx-ID: 14958 | OA
Xu, D., Yao, D., Lachapelle, S., Taslakian, P., von Kügelgen, J., Locatello, F., & Magliacane, S. (2023). A sparsity principle for partially observable causal representation learning. In Causal Representation Learning Workshop at NeurIPS 2023. New Orleans, LA, United States: OpenReview.
[Published Version] View | Files available | Download Published Version (ext.)
 

2023 | Published | Conference Paper | IST-REx-ID: 14105 | OA
Sinha, S., Gehler, P., Locatello, F., & Schiele, B. (2023). TeST: Test-time Self-Training under distribution shift. In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa, HI, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/wacv56688.2023.00278
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2023 | Submitted | Preprint | IST-REx-ID: 14207 | OA
Löwe, S., Lippe, P., Locatello, F., & Welling, M. (n.d.). Rotating features for object discovery. arXiv. https://doi.org/10.48550/arXiv.2306.00600
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2023 | Published | Conference Paper | IST-REx-ID: 14208 | OA
Zhu, Z., Liu, F., Chrysos, G. G., Locatello, F., & Cevher, V. (2023). Benign overfitting in deep neural networks under lazy training. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 43105–43128). Honolulu, Hawaii, United States: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2023 | Submitted | Preprint | IST-REx-ID: 14209 | OA
Burg, M. F., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F., & Russell, C. (n.d.). A data augmentation perspective on diffusion models and retrieval. arXiv. https://doi.org/10.48550/arXiv.2304.10253
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2023 | Submitted | Preprint | IST-REx-ID: 14210 | OA
Fumero, M., Wenzel, F., Zancato, L., Achille, A., Rodolà, E., Soatto, S., … Locatello, F. (n.d.). Leveraging sparse and shared feature activations for disentangled representation learning. arXiv. https://doi.org/10.48550/arXiv.2304.07939
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

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