Tojo, Kenji, et al. “Stealth Shaper: Reflectivity Optimization as Surface Stylization.”10.1145/3588432.3591542__SIGGRAPH 2023 Conference Proceedings, 20, Association for Computing Machinery, 2023, doi:10.1145/3588432.3591542.
LibreCat: https://research-explorer.ista.ac.at/record/14241
DOI: 10.1145/3588432.3591542
arXiv: 2305.05944

Ladle, David R., and Simon Hippenmeyer. “Loss of ET/E in Motor Neurons Leads to Reduced Monosynaptic Inputs from Proprioceptive Sensory Neurons.”10.1152/jn.00172.2022__Journal of Neurophysiology, vol. 129, no. 3, American Physiological Society, 2023, pp. 501–12, doi:10.1152/jn.00172.2022.
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PMID: 36695533

Henzinger, Thomas A., et al. “Monitoring Algorithmic Fairness.”10.1007/978-3-031-37703-7_17__Computer Aided Verification, vol. 13965, Springer Nature, 2023, pp. 358–382, doi:10.1007/978-3-031-37703-7_17.
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arXiv: 2305.15979

Glajzer, Jacek, et al. “Impact of BRCA Mutation Status on Tumor Dissemination Pattern, Surgical Outcome and Patient Survival in Primary and Recurrent High-Grade Serous Ovarian Cancer: A Multicenter Retrospective Study by the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) Consortium.”10.1245/s10434-022-12459-3__Annals of Surgical Oncology, vol. 30, Springer Nature, 2023, pp. 35–45, doi:10.1245/s10434-022-12459-3.
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Glajzer, Jacek, et al. “ASO Visual Abstract: Impact of BRCA Mutation Status on Tumor Dissemination Pattern, Surgical Outcome, and Patient Survival in Primary and Recurrent High-Grade Serous Ovarian Cancer (HGSOC). A Multicenter, Retrospective Study of the Ovarian Cancer Therapy—Innovative Models Prolong Survival (OCTIPS) Consortium.”10.1245/s10434-022-12681-z__Annals of Surgical Oncology, vol. 30, Springer Nature, 2023, pp. 46–47, doi:10.1245/s10434-022-12681-z.
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Vierra, Nicholas C., et al. “Neuronal ER-Plasma Membrane Junctions Couple Excitation to C+-Activated PKA Signaling.”10.1038/s41467-023-40930-6__Nature Communications, vol. 14, 5231, Springer Nature, 2023, doi:10.1038/s41467-023-40930-6.
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PMID: 37633939

Kretinsky, Jan, et al. “Guessing Winning Policies in LTL Synthesis by Semantic Learning.”10.1007/978-3-031-37706-8_20__35th International Conference on Computer Aided Verification , vol. 13964, Springer Nature, 2023, pp. 390–414, doi:10.1007/978-3-031-37706-8_20.
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Sinha, Samarth, et al. “TeST: Test-Time Self-Training under Distribution Shift.”10.1109/wacv56688.2023.00278__2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023, doi:10.1109/wacv56688.2023.00278.
LibreCat: https://research-explorer.ista.ac.at/record/14105
DOI: 10.1109/wacv56688.2023.00278
arXiv: 2209.11459

Mathis, S., and Lisa Annabelle Bugnet. “Asymmetries of Frequency Splittings of Dipolar Mixed Modes: A Window on the Topology of Deep Magnetic Fields.”10.1051/0004-6361/202346832__Astronomy and Astrophysics, vol. 676, L9, EDP Sciences, 2023, doi:10.1051/0004-6361/202346832.
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arXiv: 2306.11587

Gamper, Jakob, et al. “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.”10.1021/acs.jpclett.3c01707__Journal of Physical Chemistry Letters, vol. 14, no. 33, American Chemical Society, 2023, pp. 7395–403, doi:10.1021/acs.jpclett.3c01707.
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PMID: 37566743

Zhu, Zhenyu, et al. “Benign Overfitting in Deep Neural Networks under Lazy Training.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 43105–28.
LibreCat: https://research-explorer.ista.ac.at/record/14208
arXiv: 2305.19377

Burg, Max F., et al. “A Data Augmentation Perspective on Diffusion Models and Retrieval.”10.48550/arXiv.2304.10253__ArXiv, 2304.10253, doi:10.48550/arXiv.2304.10253.
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arXiv: 2304.10253

Montagna, Francesco, et al. “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.” 2nd Conference on Causal Learning and Reasoning, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14211
arXiv: 2304.03265

Montagna, Francesco, et al. “Scalable Causal Discovery with Score Matching.” 2nd Conference on Causal Learning and Reasoning, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14212
arXiv: 2304.03382

Liu, Yuejiang, et al. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” 2nd Conference on Causal Learning and Reasoning, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14214
arXiv: 2301.05169

Moschella, Luca, et al. “Relative Representations Enable Zero-Shot Latent Space Communication.” The 11th International Conference on Learning Representations, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14217
arXiv: 2209.15430

Tangemann, Matthias, et al. “Unsupervised Object Learning via Common Fate.” 2nd Conference on Causal Learning and Reasoning, 2110.06562, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14222
arXiv: 2110.06562

Seitzer, Maximilian, et al. “Bridging the Gap to Real-World Object-Centric Learning.” The 11th International Conference on Learning Representations, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14218
arXiv: 2209.14860

Zadaianchuk, Andrii, et al. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations.” The 11th International Conference on Learning Representations, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14219
arXiv: 2207.05027

Faller, Philipp M., et al. “Self-Compatibility: Evaluating Causal Discovery without Ground Truth.”10.48550/arXiv.2307.09552__ArXiv, 2307.09552, doi:10.48550/arXiv.2307.09552.
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DOI: 10.48550/arXiv.2307.09552
arXiv: 2307.09552