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83 Publications


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

2024 | Research Data Reference | IST-REx-ID: 18895 | OA
R. Cadei, F. Locatello, S. Cremer, L. Lindorfer, and C. Schmid, “ISTAnt.” Institute of Science and Technology Austria, 2024.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 

2024 | Published | Conference Paper | IST-REx-ID: 18956 | OA
B. Demirel and H. Ozkan, “Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image,” in 2024 IEEE International Conference on Image Processing, Abu Dhabi, United Arab Emirates, 2024, pp. 977–983.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18964 | OA
K. Fan et al., “Adaptive slot attention: Object discovery with dynamic slot number,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, United States, 2024.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18971 | OA
R. Arefin et al., “Unsupervised concept discovery mitigates spurious correlations,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 1672–1688.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

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

2024 | Published | Conference Paper | IST-REx-ID: 19010 | OA
D. Yao, D. Rancati, R. Cadei, M. Fumero, and F. Locatello, “Unifying causal representation learning with the invariance principle,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 38.
[Published Version] View | Files available | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 19005 | OA
D. Yao, C. J. Muller, and F. Locatello, “Marrying causal representation learning with dynamical systems for science,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 38.
[Published Version] View | Files available | arXiv
 

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

2024 | Published | Conference Paper | IST-REx-ID: 14946 | OA
D. Yao et al., “Multi-view causal representation learning with partial observability,” in 12th International Conference on Learning Representations, Vienna, Austria, 2024.
[Published Version] View | Files available | arXiv
 

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

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

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

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

2023 | Published | Conference Paper | IST-REx-ID: 14958 | OA
D. Xu et al., “A sparsity principle for partially observable causal representation learning,” in Causal Representation Learning Workshop at NeurIPS 2023, New Orleans, LA, United States, 2023.
[Published Version] View | Files available | Download Published Version (ext.)
 

2023 | Published | Conference Paper | IST-REx-ID: 14105 | OA
S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training under distribution shift,” in 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, United States, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2023 | Submitted | Preprint | IST-REx-ID: 14207 | OA
S. Löwe, P. Lippe, F. Locatello, and M. Welling, “Rotating features for object discovery,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

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

2023 | Submitted | Preprint | IST-REx-ID: 14209 | OA
M. F. Burg et al., “A data augmentation perspective on diffusion models and retrieval,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2023 | Submitted | Preprint | IST-REx-ID: 14210 | OA
M. Fumero et al., “Leveraging sparse and shared feature activations for disentangled representation learning,” arXiv. .
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

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