Sebastian Lehner

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 - 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 - 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. arXiv:2210.04248, 2022-10-09. (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)

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. arXiv:2205.12258, 2022-05-24. (more) (download)

A. Mayr, S. Lehner, A. Mayrhofer, C. Kloss, S. Hochreiter, and J. Brandstetter (2021) Boundary Graph Neural Networks for 3D Simulations. arXiv:2106.11299, 2021-06-21. (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)