Divided attention: Unsupervised multi-object discovery with contextually separated slots

Multi-view causal representation learning with partial observability
D. Yao, D. Xu, S. Lachapelle, S. Magliacane, P. Taslakian, G. Martius, J. von Kügelgen, F. Locatello, ArXiv (n.d.).

Grounded object centric learning
A. Kori, F. Locatello, F.D.S. Ribeiro, F. Toni, B. Glocker, ArXiv (n.d.).

Image retrieval outperforms diffusion models on data augmentation
M. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, Journal of Machine Learning Research (2023).

Latent space translation via semantic alignment
V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, E. Rodolà, ArXiv (n.d.).

Sample complexity bounds for score-matching: Causal discovery and generative modeling
Z. Zhu, F. Locatello, V. Cevher, ArXiv (n.d.).

Assumption violations in causal discovery and the robustness of score matching
F. Montagna, A.A. Mastakouri, E. Eulig, N. Noceti, L. Rosasco, D. Janzing, B. Aragam, F. Locatello, ArXiv (n.d.).

A sparsity principle for partially observable causal representation learning
D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello, S. Magliacane, in:, Causal Representation Learning Workshop at NeurIPS 2023, OpenReview, 2023.

Shortcuts for causal discovery of nonlinear models by score matching
F. Montagna, N. Noceti, L. Rosasco, F. Locatello, ArXiv (n.d.).

Unsupervised open-vocabulary object localization in videos
K. Fan, Z. Bai, T. Xiao, D. Zietlow, M. Horn, Z. Zhao, C.-J.S.-G. Carl-Johann Simon-Gabriel, M.Z. Shou, F. Locatello, B. Schiele, T. Brox, Z. Zhang, Y. Fu, T. He, ArXiv (n.d.).

Object-centric multiple object tracking
Z. Zhao, J. Wang, M. Horn, Y. Ding, T. He, Z. Bai, D. Zietlow, C.-J.S.-G. Carl-Johann Simon-Gabriel, B. Shuai, Z. Tu, T. Brox, B. Schiele, Y. Fu, F. Locatello, Z. Zhang, T. Xiao, ArXiv (n.d.).

TeST: Test-time Self-Training under distribution shift
S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023.

Rotating features for object discovery
S. Löwe, P. Lippe, F. Locatello, M. Welling, ArXiv (n.d.).

Benign overfitting in deep neural networks under lazy training
Z. Zhu, F. Liu, G.G. Chrysos, F. Locatello, V. Cevher, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 43105–43128.

A data augmentation perspective on diffusion models and retrieval
M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, ArXiv (n.d.).

Leveraging sparse and shared feature activations for disentangled representation learning
M. Fumero, F. Wenzel, L. Zancato, A. Achille, E. Rodolà, S. Soatto, B. Schölkopf, F. Locatello, ArXiv (n.d.).

Causal discovery with score matching on additive models with arbitrary noise
F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.

Scalable causal discovery with score matching
F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.

Causal triplet: An open challenge for intervention-centric causal representation learning
Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.

Relative representations enable zero-shot latent space communication
L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, E. Rodolà, in:, The 11th International Conference on Learning Representations, 2023.