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Michael Volpp
Michael Volpp
Karlsruhe Institute of Technology, Bosch Center for Artificial Intelligence
Verified email at kit.edu
Title
Cited by
Cited by
Year
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
49*2019
Bayesian Context Aggregation for Neural Processes
M Volpp, F Flürenbrock, L Grossberger, C Daniel, G Neumann
ICLR 2021, 2021
122021
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
52022
Trajectory-Based Off-Policy Deep Reinforcement Learning
A Doerr, M Volpp, M Toussaint, S Trimpe, C Daniel
ICML 2019, 2019
52019
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
42015
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
42014
ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives
G Li, Z Jin, M Volpp, F Otto, R Lioutikov, G Neumann
arXiv preprint arXiv:2210.01531, 2022
12022
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models
O Arenz, P Dahlinger, Z Ye, M Volpp, G Neumann
arXiv preprint arXiv:2209.11533, 2022
2022
Configuring a system which interacts with an environment
A Doerr, C Daniel, M Volpp
US Patent 11,402,808, 2022
2022
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
2022
Method and device for training a machine learning system
G Neumann, M Volpp
US Patent App. 17/449,517, 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
Stable Optimization of Gaussian Likelihoods
D Megerle, F Otto, M Volpp, G Neumann
Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference
M Volpp, P Dahlinger, P Becker, C Daniel, G Neumann
International Conference on Learning Representations, 0
Supplementary Material for: What Matters For Meta-Learning Vision Regression Tasks?
N Gao, H Ziesche, NA Vien, M Volpp, G Neumann
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Articles 1–16