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9719 Publications
2024 | Conference Paper | LibreCat-ID: 50807
Hu, H., Liu, F., Pei, Q., Yuan, Y., Xu, Z., & Wang, L. (2024). 𝜆Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing. Proceedings of the ACM Web Conference (WWW). ACM Web Conference (WWW), Singapore.
LibreCat
2024 | Preprint | LibreCat-ID: 51160 | 
Philipp, F. M., Schaller, M., Boshoff, S., Peitz, S., Nüske, F., & Worthmann, K. (2024). Extended Dynamic Mode Decomposition: Sharp bounds on the sample efficiency. In arXiv:2402.02494.
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| arXiv
2024 | Journal Article | LibreCat-ID: 51208
Gebken, B. (2024). A note on the convergence of deterministic gradient sampling in nonsmooth optimization. Computational Optimization and Applications. https://doi.org/10.1007/s10589-024-00552-0
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| DOI
2024 | Journal Article | LibreCat-ID: 51374
Hasler, D., Hinrichs, B., & Siebert, O. (2024). Non-Fock ground states in the translation-invariant Nelson model revisited non-perturbatively. Journal of Functional Analysis, 286(7), Article 110319. https://doi.org/10.1016/j.jfa.2024.110319
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| DOI
| arXiv
2024 | Journal Article | LibreCat-ID: 32101 |
Weich, T., Guedes Bonthonneau, Y., Guillarmou, C., & Hilgert, J. (2024). Ruelle-Taylor resonaces of Anosov actions. J. Europ. Math. Soc., 1–36.
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2024 | Preprint | LibreCat-ID: 51501 |
Hilgert, J. (2024). Quantum-Classical Correspondences for Locally Symmetric Spaces.
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2024 | Journal Article | LibreCat-ID: 46019 |
Sonntag, K., & Peitz, S. (2024). Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems. Journal of Optimization Theory and Applications. https://doi.org/10.1007/s10957-024-02389-3
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2024 | Preprint | LibreCat-ID: 51334 |
Sonntag, K., Gebken, B., Müller, G., Peitz, S., & Volkwein, S. (2024). A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces. In arXiv:2402.06376.
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| arXiv
2024 | Journal Article | LibreCat-ID: 40171 |
Peitz, S., Stenner, J., Chidananda, V., Wallscheid, O., Brunton, S. L., & Taira, K. (2024). Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning. Physica D: Nonlinear Phenomena, 461, 134096. https://doi.org/10.1016/j.physd.2024.134096
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