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8915 Publications


2023 | Submitted | Preprint | IST-REx-ID: 14948 | OA
Kori A, Locatello F, Ribeiro FDS, Toni F, Glocker B. Grounded object centric learning. arXiv. doi:10.48550/arXiv.2307.09437
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2023 | Published | Journal Article | IST-REx-ID: 14949 | OA
Burg M, Wenzel F, Zietlow D, et al. Image retrieval outperforms diffusion models on data augmentation. Journal of Machine Learning Research. 2023.
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2023 | Submitted | Preprint | IST-REx-ID: 14952 | OA
Maiorca V, Moschella L, Norelli A, Fumero M, Locatello F, Rodolà E. Latent space translation via semantic alignment. arXiv. doi:10.48550/arXiv.2311.00664
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2023 | Submitted | Preprint | IST-REx-ID: 14953 | OA
Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching: Causal discovery and generative modeling. arXiv. doi:10.48550/arXiv.2310.18123
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2023 | Submitted | Preprint | IST-REx-ID: 14954 | OA
Montagna F, Mastakouri AA, Eulig E, et al. Assumption violations in causal discovery and the robustness of score matching. arXiv. doi:10.48550/arXiv.2310.13387
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2023 | Published | Conference Paper | IST-REx-ID: 14958 | OA
Xu D, Yao D, Lachapelle S, et al. A sparsity principle for partially observable causal representation learning. In: Causal Representation Learning Workshop at NeurIPS 2023. OpenReview; 2023.
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2023 | Submitted | Preprint | IST-REx-ID: 14961 | OA
Montagna F, Noceti N, Rosasco L, Locatello F. Shortcuts for causal discovery of nonlinear models by score matching. arXiv. doi:10.48550/arXiv.2310.14246
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2023 | Submitted | Preprint | IST-REx-ID: 14962 | OA
Fan K, Bai Z, Xiao T, et al. Unsupervised open-vocabulary object localization in videos. arXiv. doi:10.48550/arXiv.2309.09858
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2023 | Submitted | Preprint | IST-REx-ID: 14963 | OA
Zhao Z, Wang J, Horn M, et al. Object-centric multiple object tracking. arXiv. doi:10.48550/arXiv.2309.00233
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2023 | Published | Journal Article | IST-REx-ID: 14985 | OA
Liu Z, Hong T, Xu L, et al. Lattice expansion enables interstitial doping to achieve a high average ZT in n‐type PbS. Interdisciplinary Materials. 2023;2(1):161-170. doi:10.1002/idm2.12056
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2023 | Published | Conference Paper | IST-REx-ID: 14989 | OA
Malvai H, Kokoris Kogias E, Sonnino A, et al. Parakeet: Practical key transparency for end-to-end eEncrypted messaging. In: Proceedings of the 2023 Network and Distributed System Security Symposium. Internet Society; 2023. doi:10.14722/ndss.2023.24545
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2023 | Research Data Reference | IST-REx-ID: 14990 | OA
Meggendorfer T. Artefact for: Correct Approximation of Stationary Distributions. 2023. doi:10.5281/ZENODO.7548214
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2023 | Research Data Reference | IST-REx-ID: 14991 | OA
Hwong Y-L, Colin M, Aglas P, Muller CJ, Sherwood SC. Data-assessing memory in convection schemes using idealized tests. 2023. doi:10.5281/ZENODO.7757041
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2023 | Published | Book Chapter | IST-REx-ID: 14992 | OA
Lewin M, Lieb EH, Seiringer R. Universal Functionals in Density Functional Theory. In: Cances E, Friesecke G, eds. Density Functional Theory. 1st ed. MAMOMO. Springer; 2023:115-182. doi:10.1007/978-3-031-22340-2_3
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2023 | Published | Conference Paper | IST-REx-ID: 14993 | OA
Currin C, Asiedu MN, Fourie C, et al. A framework for grassroots research collaboration in machine learning and global health. In: 1st Workshop on Machine Learning & Global Health. OpenReview; 2023.
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2023 | Research Data Reference | IST-REx-ID: 14994 | OA
Majumdar R, Mallik K, Rychlicki M, Schmuck A-K, Soudjani S. A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties. 2023. doi:10.5281/ZENODO.7877790
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2023 | Research Data Reference | IST-REx-ID: 14995 | OA
Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. Lincheck: A practical framework for testing concurrent data structures on JVM. 2023. doi:10.5281/ZENODO.7877757
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2023 | Research Data Reference | IST-REx-ID: 15027 | OA
Curk S. aggregation_data. 2023.
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2023 | Research Data Reference | IST-REx-ID: 15035 | OA
Chalupa M, Henzinger TA. Monitoring hyperproperties with prefix transducers. 2023. doi:10.5281/ZENODO.8191723
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2023 | Submitted | Preprint | IST-REx-ID: 15039 | OA
Prach B, Lampert C. 1-Lipschitz neural networks are more expressive with N-activations. arXiv. doi:10.48550/ARXIV.2311.06103
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