Sebastian Lehner

A. Mayr, S. Lehner, A. Mayrhofer, C. Kloss, S. Hochreiter, and J. Brandstetter (2023) Boundary Graph Neural Networks for 3D Simulations. Proceedings of the AAAI Conference on Artificial Intelligence, 37, 8, 9099-9107, 2023-06-26. (more) (download)

L. Lewis, H.-Y. Huang, V. T. Tran, S. Lehner, R. Kueng, and J. Preskill (2023) Improved Machine Learning Algorithm for Predicting Ground State Properties. arXiv:2301.13169, 2023-01-30. (more) (download)

V. T. Tran, L. Lewis, H.-Y. Huang, J. Kofler, R. Kueng, S. Hochreiter, and S. Lehner (2022) Using Shadows to Learn Ground State Properties of Quantum Hamiltonians. Machine Learning and the Physical Sciences at NeurIPS 2022, 2022-12-03. (more) (download)

S. Sanokowski, W. Berghammer, J. Kofler, S. Hochreiter, and S. Lehner (2022) One Network to Approximate Them All: Amortized Variational Inference of Ising Ground States. Machine Learning and the Physical Sciences at NeurIPS 2022, 2022-12-03. (more) (download)

P. M. Winter, C. Burger, S. Lehner, J. Kofler, T. I. Maindl, and C. M. Schäfer (2022) Residual Neural Networks for the Prediction of Planetary Collision Outcomes. Monthly Notices of the Royal Astronomical Society, 520, 1, 1224-1242, 2022-10-14. (more) (download)

F. Paischer, T. Adler, V. Patil, A. Bitto-Nemling, M. Holzleitner, S. Lehner, H. Eghbal-zadeh, and S. Hochreiter (2022) History Compression via Language Models in Reinforcement Learning. Proceedings of the 39th International Conference on Machine Learning, PMLR, 162, 17156-17185, 2022-06-28. (more) (download)

M. Gauch, M. Beck, T. Adler, D. Kotsur, S. Fiel, H. Eghbal-zadeh, J. Brandstetter, J. Kofler, M. Holzleitner, W. Zellinger, D. Klotz, S. Hochreiter, and S. Lehner (2022) Few-Shot Learning by Dimensionality Reduction in Gradient Space. arXiv:2206.03483, 2022-06-07. (more) (download)

A. Mayr, S. Lehner, A. Mayrhofer, C. Kloss, S. Hochreiter, and J. Brandstetter (2021) Learning 3D Granular Flow Simulations. arXiv: 2105.01636, 2021-05-04. (more) (download)