| Tensorforce: A tensorflow library for applied reinforcement learning M Schaarschmidt, A Kuhnle, K Fricke Web page, 2017 | 191* | 2017 |
| Simple gnn regularisation for 3d molecular property prediction and beyond J Godwin, M Schaarschmidt, AL Gaunt, A Sanchez-Gonzalez, ... International conference on learning representations, 2021 | 138 | 2021 |
| Pre-training via denoising for molecular property prediction S Zaidi, M Schaarschmidt, J Martens, H Kim, YW Teh, ... arXiv preprint arXiv:2206.00133, 2022 | 134 | 2022 |
| BOAT: Building auto-tuners with structured Bayesian optimization V Dalibard, M Schaarschmidt, E Yoneki Proceedings of the 26th International Conference on World Wide Web, 479-488, 2017 | 112 | 2017 |
| Reinforcement learning for the adaptive scheduling of educational activities J Bassen, B Balaji, M Schaarschmidt, C Thille, J Painter, D Zimmaro, ... Proceedings of the 2020 CHI conference on human factors in computing systems …, 2020 | 89 | 2020 |
| Lift: Reinforcement learning in computer systems by learning from demonstrations M Schaarschmidt, A Kuhnle, B Ellis, K Fricke, F Gessert, E Yoneki arXiv preprint arXiv:1808.07903, 2018 | 47 | 2018 |
| Towards automated polyglot persistence M Schaarschmidt, F Gessert, N Ritter Datenbanksysteme für Business, Technologie und Web (BTW 2015), 73-82, 2015 | 44 | 2015 |
| Quaestor: Query web caching for database-as-a-service providers F Gessert, M Schaarschmidt, W Wingerath, E Witt, E Yoneki, N Ritter Proceedings of the VLDB Endowment 10 (12), 1670-1681, 2017 | 31 | 2017 |
| Towards a Scalable and Unified REST API for Cloud Data Stores. F Gessert, S Friedrich, W Wingerath, M Schaarschmidt, N Ritter GI-Jahrestagung, 723-734, 2014 | 28 | 2014 |
| Very deep graph neural networks via noise regularisation J Godwin, M Schaarschmidt, A Gaunt, A Sanchez-Gonzalez, Y Rubanova, ... arXiv preprint arXiv:2106.07971, 2021 | 25 | 2021 |
| Rlgraph: modular computation graphs for deep reinforcement learning M Schaarschmidt, S Mika, K Fricke, E Yoneki Proceedings of Machine Learning and Systems 1, 65-80, 2019 | 23* | 2019 |
| The cache sketch: Revisiting expiration-based caching in the age of cloud data management F Gessert, M Schaarschmidt, W Wingerath, S Friedrich, N Ritter Datenbanksysteme für Business, Technologie und Web (BTW 2015), 53-72, 2015 | 13 | 2015 |
| Learned force fields are ready for ground state catalyst discovery M Schaarschmidt, M Riviere, AM Ganose, JS Spencer, AL Gaunt, ... arXiv preprint arXiv:2209.12466, 2022 | 12 | 2022 |
| Automap: Towards ergonomic automated parallelism for ML models M Schaarschmidt, D Grewe, D Vytiniotis, A Paszke, GS Schmid, T Norman, ... arXiv preprint arXiv:2112.02958, 2021 | 11 | 2021 |
| Learning index selection with structured action spaces J Welborn, M Schaarschmidt, E Yoneki arXiv preprint arXiv:1909.07440, 2019 | 10 | 2019 |
| Learning runtime parameters in computer systems with delayed experience injection M Schaarschmidt, F Gessert, V Dalibard, E Yoneki arXiv preprint arXiv:1610.09903, 2016 | 10 | 2016 |
| Tensorforce: a TensorFlow library for applied reinforcement learning. Web page.(2017) A Kuhnle, M Schaarschmidt, K Fricke Dostupné tiež z: https://tensorforce. readthedocs. io/en/latest/basics/tune …, 2017 | 8 | 2017 |
| Wield: Systematic reinforcement learning with progressive randomization M Schaarschmidt, K Fricke, E Yoneki arXiv preprint arXiv:1909.06844, 2019 | 4 | 2019 |
| Partir: Composing spmd partitioning strategies for machine learning S Alabed, D Belov, B Chrzaszcz, J Franco, D Grewe, D Maclaurin, ... arXiv preprint arXiv:2401.11202, 2024 | 3 | 2024 |
| Tuning the scheduling of distributed stochastic gradient descent with Bayesian optimization V Dalibard, M Schaarschmidt, E Yoneki arXiv preprint arXiv:1612.00383, 2016 | 2 | 2016 |
Kai R. FrickeDepartment of Personality Psychology and Psychological Assessment, Helmut-Schmidt-University HamburgVerified email at kaifricke.com
