Mathis S, Bugnet LA. Asymmetries of frequency splittings of dipolar mixed modes: A window on the topology of deep magnetic fields. Astronomy and Astrophysics. 2023;676. doi:10.1051/0004-6361/202346832
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DOI: 10.1051/0004-6361/202346832
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arXiv: 2306.11587

Gamper J, Kluibenschedl F, Weiss AKH, Hofer TS. Accessing position space wave functions in band structure calculations of periodic systems - a generalized, adapted numerov implementation for one-, two-, and three-dimensional quantum problems. Journal of Physical Chemistry Letters. 2023;14(33):7395-7403. doi:10.1021/acs.jpclett.3c01707
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PMID: 37566743

Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. Benign overfitting in deep neural networks under lazy training. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:43105-43128.
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arXiv: 2305.19377

Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion models and retrieval. arXiv. doi:10.48550/arXiv.2304.10253
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Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Causal discovery with score matching on additive models with arbitrary noise. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
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arXiv: 2304.03265

Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Scalable causal discovery with score matching. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
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arXiv: 2304.03382

Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric causal representation learning. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
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arXiv: 2301.05169

Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. Relative representations enable zero-shot latent space communication. In: The 11th International Conference on Learning Representations. ; 2023.
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Tangemann M, Schneider S, Kügelgen J von, et al. Unsupervised object learning via common fate. In: 2nd Conference on Causal Learning and Reasoning. ; 2023.
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arXiv: 2110.06562

Seitzer M, Horn M, Zadaianchuk A, et al. Bridging the gap to real-world object-centric learning. In: The 11th International Conference on Learning Representations. ; 2023.
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arXiv: 2209.14860

Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. Unsupervised semantic segmentation with self-supervised object-centric representations. In: The 11th International Conference on Learning Representations. ; 2023.
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arXiv: 2207.05027

Faller PM, Vankadara LC, Mastakouri AA, Locatello F, Janzing D. Self-compatibility: Evaluating causal discovery without ground truth. arXiv. doi:10.48550/arXiv.2307.09552
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Boocock DR, Hirashima T, Hannezo EB. Interplay between mechanochemical patterning and glassy dynamics in cellular monolayers. PRX Life. 2023;1(1). doi:10.1103/prxlife.1.013001
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Nardin M, Käfer K, Stella F, Csicsvari JL. Theta oscillations as a substrate for medial prefrontal-hippocampal assembly interactions. Cell Reports. 2023;42(9). doi:10.1016/j.celrep.2023.113015
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Cho UH, Hetzer M. Caspase-mediated nuclear pore complex trimming in cell differentiation and endoplasmic reticulum stress. eLife. 2023;12. doi:10.7554/eLife.89066
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Anastos M, Fabian D, Müyesser A, Szabó T. Splitting matchings and the Ryser-Brualdi-Stein conjecture for multisets. Electronic Journal of Combinatorics. 2023;30(3). doi:10.37236/11714
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arXiv: 2212.03100

Sun Y, Fu H, Chatterjee K, Goharshady AK. Automated tail bound analysis for probabilistic recurrence relations. In: Computer Aided Verification. Vol 13966. Springer Nature; 2023:16-39. doi:10.1007/978-3-031-37709-9_2
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Akshay S, Chatterjee K, Meggendorfer T, Zikelic D. MDPs as distribution transformers: Affine invariant synthesis for safety objectives. In: International Conference on Computer Aided Verification. Vol 13966. Springer Nature; 2023:86-112. doi:10.1007/978-3-031-37709-9_5
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Nagano M, Aoshima K, Shimamura H, Siekhaus DE, Toshima JY, Toshima J. Distinct role of TGN-resident clathrin adaptors for Vpp activation in the TGN-endosome trafficking pathway. Journal of Cell Science. 2023;136(17). doi:10.1242/jcs.261448
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Henderson PM, Ghazaryan A, Zibrov AA, Young AF, Serbyn M. Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene. Physical Review B. 2023;108(12). doi:10.1103/physrevb.108.125411
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arXiv: 2210.06310