| Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization M Volpp, LP Fröhlich, K Fischer, A Doerr, S Falkner, F Hutter, C Daniel ICLR 2020, 2019 | 82 | 2019 |
| Prodmp: A unified perspective on dynamic and probabilistic movement primitives G Li, Z Jin, M Volpp, F Otto, R Lioutikov, G Neumann IEEE Robotics and Automation Letters 8 (4), 2325-2332, 2023 | 30 | 2023 |
| Bayesian Context Aggregation for Neural Processes M Volpp, F Flürenbrock, L Grossberger, C Daniel, G Neumann ICLR 2021, 2021 | 30 | 2021 |
| What matters for meta-learning vision regression tasks? N Gao, H Ziesche, NA Vien, M Volpp, G Neumann Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 28 | 2022 |
| Factorization with a logarithmic energy spectrum of a two-dimensional potential F Gleisberg, M Volpp, WP Schleich Physics Letters A 379 (40-41), 2556-2560, 2015 | 7 | 2015 |
| Trajectory-Based Off-Policy Deep Reinforcement Learning A Doerr, M Volpp, M Toussaint, S Trimpe, C Daniel ICML 2019, 2019 | 6 | 2019 |
| A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models O Arenz, P Dahlinger, Z Ye, M Volpp, G Neumann TMLR 2023, 2023 | 5 | 2023 |
| Beyond Deep Ensembles--A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift F Seligmann, P Becker, M Volpp, G Neumann NeurIPS 2023, 2023 | 4 | 2023 |
| Standard development process for physical models used in real time applications based on the example of an exhaust pipe model A Gallet, M Volpp, W Lengerer Technical report, Robert Bosch GmbH, 2014 | 4 | 2014 |
| Method and device for training a machine learning system G Neumann, M Volpp US Patent App. 17/449,517, 2022 | 3 | 2022 |
| Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference M Volpp, P Dahlinger, P Becker, C Daniel, G Neumann ICLR 2023, 2023 | 2 | 2023 |
| Method for ascertaining an output signal with the aid of a machine learning system G Neumann, M Volpp US Patent App. 17/449,139, 2022 | 2 | 2022 |
| Stable Optimization of Gaussian Likelihoods D Megerle, F Otto, M Volpp, G Neumann | 1 | 2023 |
| Method for estimating model uncertainties with the aid of a neural network and an architecture of the neural network G Neumann, M Volpp US Patent App. 18/349,571, 2024 | | 2024 |
| Latent Task-Specific Graph Network Simulators P Dahlinger, N Freymuth, M Volpp, T Hoang, G Neumann arXiv preprint arXiv:2311.05256, 2023 | | 2023 |
| Method for assessing model uncertainties with the aid of a neural network and an architecture of the neural network G Neumann, M Volpp US Patent App. 18/187,128, 2023 | | 2023 |
| Method for training a conditional neural process for determining a position of an object from image data N Gao, AV Ngo, G Neumann, H Ziesche, M Volpp US Patent App. 18/167,733, 2023 | | 2023 |
| Configuring a system which interacts with an environment A Doerr, C Daniel, M Volpp US Patent 11,402,808, 2022 | | 2022 |
| Bayesian context aggregation for neural processes G Neumann, M Volpp US Patent App. 17/446,676, 2022 | | 2022 |
| Running of Radiative Neutrino Masses - A Study of the Zee-Babu Model M Volpp Max Planck Institute for Physics Munich, 2017 | | 2017 |
Christian DanielBosch Center for Artificial Intelligence在 ias.tu-darmstadt.de 的电子邮件经过验证
