Hopfield networks is all you need H Ramsauer, B Schäfl, J Lehner, P Seidl, M Widrich, T Adler, L Gruber, ... arXiv preprint arXiv:2008.02217, 2020 | 430 | 2020 |
Modern hopfield networks and attention for immune repertoire classification M Widrich, B Schäfl, M Pavlović, H Ramsauer, L Gruber, M Holzleitner, ... Advances in neural information processing systems 33, 18832-18845, 2020 | 114 | 2020 |
Mc-lstm: Mass-conserving lstm PJ Hoedt, F Kratzert, D Klotz, C Halmich, M Holzleitner, GS Nearing, ... International conference on machine learning, 4275-4286, 2021 | 66 | 2021 |
Convergence proof for actor-critic methods applied to PPO and RUDDER M Holzleitner, L Gruber, J Arjona-Medina, J Brandstetter, S Hochreiter Transactions on Large-Scale Data-and Knowledge-Centered Systems XLVIII …, 2021 | 30 | 2021 |
History compression via language models in reinforcement learning F Paischer, T Adler, V Patil, A Bitto-Nemling, M Holzleitner, S Lehner, ... International Conference on Machine Learning, 17156-17185, 2022 | 29 | 2022 |
Dispersion Estimates for Spherical Schr\" odinger Equations: The Effect of Boundary Conditions M Holzleitner, A Kostenko, G Teschl arXiv preprint arXiv:1601.01638, 2016 | 19 | 2016 |
Transformation operators for spherical Schrödinger operators M Holzleitner Journal of Mathematical Analysis and Applications 481 (1), 123430, 2020 | 12 | 2020 |
Hopfield networks is all you need. arXiv 2020 H Ramsauer, B Schäfl, J Lehner, P Seidl, M Widrich, T Adler, L Gruber, ... arXiv preprint arXiv:2008.02217, 0 | 12 | |
Addressing parameter choice issues in unsupervised domain adaptation by aggregation MC Dinu, M Holzleitner, M Beck, HD Nguyen, A Huber, H Eghbal-zadeh, ... arXiv preprint arXiv:2305.01281, 2023 | 10 | 2023 |
Zero energy scattering for one-dimensional Schrödinger operators and applications to dispersive estimates I Egorova, M Holzleitner, G Teschl Proceedings of the American Mathematical Society, Series B 2 (4), 51-59, 2015 | 6 | 2015 |
Few-shot learning by dimensionality reduction in gradient space M Gauch, M Beck, T Adler, D Kotsur, S Fiel, H Eghbal-zadeh, ... Conference on Lifelong Learning Agents, 1043-1064, 2022 | 5 | 2022 |
Dispersion Estimates for Spherical Schr\" odinger Equations with Critical Angular Momentum M Holzleitner, A Kostenko, G Teschl arXiv preprint arXiv:1611.05210, 2016 | 4 | 2016 |
Domain Generalization by Functional Regression M Holzleitner, SV Pereverzyev, W Zellinger Numerical Functional Analysis and Optimization, 1-23, 2024 | 2 | 2024 |
Properties of the scattering matrix and dispersion estimates for Jacobi operators I Egorova, M Holzleitner, G Teschl Journal of Mathematical Analysis and Applications 434 (1), 956-966, 2016 | 2 | 2016 |
On regularized polynomial functional regression M Holzleitner, SV Pereverzyev Journal of Complexity 83, 101853, 2024 | 1 | 2024 |
Universal Physics Transformers B Alkin, A Fürst, S Schmid, L Gruber, M Holzleitner, J Brandstetter arXiv preprint arXiv:2402.12365, 2024 | 1 | 2024 |
InfODist: Online distillation with Informative rewards improves generalization in Curriculum Learning R Siripurapu, VP Patil, K Schweighofer, MC Dinu, T Schmied, LEF Diez, ... Deep Reinforcement Learning Workshop NeurIPS 2022, 2022 | 1 | 2022 |
Multiparameter regularization and aggregation in the context of polynomial functional regression ER Gizewski, M Holzleitner, L Mayer-Suess, S Pereverzyev Jr, ... arXiv preprint arXiv:2405.04147, 2024 | | 2024 |
Overcoming Saturation in Density Ratio Estimation by Iterated Regularization L Gruber, M Holzleitner, J Lehner, S Hochreiter, W Zellinger arXiv preprint arXiv:2402.13891, 2024 | | 2024 |
SymbolicAI: A framework for logic-based approaches combining generative models and solvers MC Dinu, C Leoveanu-Condrei, M Holzleitner, W Zellinger, S Hochreiter arXiv preprint arXiv:2402.00854, 2024 | | 2024 |