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6216 Publications
2023 | Published | Thesis | IST-REx-ID: 12470 |
Michalska, J. M. (2023). A versatile toolbox for the comprehensive analysis of nervous tissue organization with light microscopy. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12470
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2023 | Published | Journal Article | IST-REx-ID: 12521 |
Mrnjavac, A., Khudiakova, K., Barton, N. H., & Vicoso, B. (2023). Slower-X: Reduced efficiency of selection in the early stages of X chromosome evolution. Evolution Letters. Oxford University Press. https://doi.org/10.1093/evlett/qrac004
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2023 | Published | Journal Article | IST-REx-ID: 14656 |
Nardin, M., Csicsvari, J. L., Tkačik, G., & Savin, C. (2023). The structure of hippocampal CA1 interactions optimizes spatial coding across experience. The Journal of Neuroscience. Society for Neuroscience. https://doi.org/10.1523/JNEUROSCI.0194-23.2023
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2023 | Published | Journal Article | IST-REx-ID: 13117 |
Redchenko, E., Poshakinskiy, A. V., Sett, R., Zemlicka, M., Poddubny, A. N., & Fink, J. M. (2023). Tunable directional photon scattering from a pair of superconducting qubits. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-023-38761-6
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| arXiv
2023 | Published | Thesis | IST-REx-ID: 14323 |
Kuzmicz-Kowalska, K. (2023). Regulation of neural progenitor survival by Shh and BMP in the developing spinal cord. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:14323
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2023 | Published | Journal Article | IST-REx-ID: 12837 |
Bocanegra, L., Singh, A., Hannezo, E. B., Zagórski, M. P., & Kicheva, A. (2023). Cell cycle dynamics control fluidity of the developing mouse neuroepithelium. Nature Physics. Springer Nature. https://doi.org/10.1038/s41567-023-01977-w
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2023 | Published | Thesis | IST-REx-ID: 13081 |
Bocanegra, L. (2023). Epithelial dynamics during mouse neural tube development. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13081
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2023 | Published | Journal Article | IST-REx-ID: 13136 |
Minchington, T., Rus, S., & Kicheva, A. (2023). Control of tissue dimensions in the developing neural tube and somites. Current Opinion in Systems Biology. Elsevier. https://doi.org/10.1016/j.coisb.2023.100459
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2023 | Published | Conference Paper | IST-REx-ID: 14459 |
Shevchenko, A., Kögler, K., Hassani, H., & Mondelli, M. (2023). Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 31151–31209). Honolulu, Hawaii, HI, United States: ML Research Press.
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2023 | Published | Journal Article | IST-REx-ID: 13097 |
Wilson, A. C., & Sweeney, L. B. (2023). Spinal cords: Symphonies of interneurons across species. Frontiers in Neural Circuits. Frontiers. https://doi.org/10.3389/fncir.2023.1146449
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2023 | Published | Conference Paper | IST-REx-ID: 13143 |
Hoffmann, C., Hubáček, P., Kamath, C., & Pietrzak, K. Z. (2023). Certifying giant nonprimes. In Public-Key Cryptography - PKC 2023 (Vol. 13940, pp. 530–553). Atlanta, GA, United States: Springer Nature. https://doi.org/10.1007/978-3-031-31368-4_19
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2022 | Published | Journal Article | IST-REx-ID: 13240 |
Ingole, K. D., Nagarajan, N., Uhse, S., Giannini, C., & Djamei, A. (2022). Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis. Frontiers in Fungal Biology. Frontiers Media. https://doi.org/10.3389/ffunb.2022.1029114
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2022 | Published | Conference Paper | IST-REx-ID: 13241 |
Konstantinov, N. H., & Lampert, C. (2022). On the impossibility of fairness-aware learning from corrupted data. In Proceedings of Machine Learning Research (Vol. 171, pp. 59–83). ML Research Press.
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2022 | Published | Conference Paper | IST-REx-ID: 14093 |
Dresdner, G., Vladarean, M.-L., Rätsch, G., Locatello, F., Cevher, V., & Yurtsever, A. (2022). Faster one-sample stochastic conditional gradient method for composite convex minimization. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (Vol. 151, pp. 8439–8457). Virtual: ML Research Press.
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2022 | Published | Conference Paper | IST-REx-ID: 14106 |
Lohaus, M., Kleindessner, M., Kenthapadi, K., Locatello, F., & Russell, C. (2022). Are two heads the same as one? Identifying disparate treatment in fair neural networks. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 16548–16562). New Orleans, LA, United States: Neural Information Processing Systems Foundation.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 14107 |
Yao, J., Hong, Y., Wang, C., Xiao, T., He, T., Locatello, F., … Zhang, Z. (2022). Self-supervised amodal video object segmentation. In 36th Conference on Neural Information Processing Systems. New Orleans, LA, United States. https://doi.org/10.48550/arXiv.2210.12733
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2022 | Published | Conference Paper | IST-REx-ID: 14114 |
Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F., Scholkopf, B., & Russell, C. (2022). Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10400–10411). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01016
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2022 | Published | Conference Paper | IST-REx-ID: 14168 |
Rahaman, N., Weiss, M., Locatello, F., Pal, C., Bengio, Y., Schölkopf, B., … Ballas, N. (2022). Neural attentive circuits. In 36th Conference on Neural Information Processing Systems (Vol. 35). New Orleans, United States.
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| arXiv
2022 | Submitted | Conference Paper | IST-REx-ID: 14170 |
Dittadi, A., Papa, S., Vita, M. D., Schölkopf, B., Winther, O., & Locatello, F. (n.d.). Generalization and robustness implications in object-centric learning. In Proceedings of the 39th International Conference on Machine Learning (Vol. 2022, pp. 5221–5285). Baltimore, MD, United States: ML Research Press.
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2022 | Published | Conference Paper | IST-REx-ID: 14171 |
Rolland, P., Cevher, V., Kleindessner, M., Russel, C., Schölkopf, B., Janzing, D., & Locatello, F. (2022). Score matching enables causal discovery of nonlinear additive noise models. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162, pp. 18741–18753). Baltimore, MD, United States: ML Research Press.
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