Manuel Haussmann
Manuel Haussmann
PhD Student, Heidelberg University
Verified email at mailbox.org - Homepage
Title
Cited by
Cited by
Year
Deep-learning jets with uncertainties and more
S Bollweg, M Hau▀mann, G Kasieczka, M Luchmann, T Plehn, ...
SciPost Phys 8 (006), 1904.10004, 2020
172020
Variational Bayesian Multiple Instance Learning with Gaussian Processes
M Hau▀mann, FA Hamprecht, M Kandemir
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6570-6579, 2017
122017
Deep active learning with adaptive acquisition
M Hau▀mann, FA Hamprecht, M Kandemir
arXiv preprint arXiv:1906.11471, 2019
92019
Sampling-free variational inference of bayesian neural networks by variance backpropagation
M Hau▀mann, FA Hamprecht, M Kandemir
Uncertainty in Artificial Intelligence, 563-573, 2020
7*2020
Variational Weakly Supervised Gaussian Processes.
M Kandemir, M Haussmann, F Diego, KT Rajamani, J Van Der Laak, ...
BMVC, 2016
72016
LeMoNADe: learned motif and neuronal assembly detection in calcium imaging videos
E Kirschbaum, M Hau▀mann, S Wolf, H Sonntag, J Schneider, S Elzoheiry, ...
arXiv preprint arXiv:1806.09963, 2018
32018
Bayesian Evidential Deep Learning with PAC Regularization
M Haussmann, S Gerwinn, M Kandemir
arXiv preprint arXiv:1906.00816, 2019
1*2019
Control and monitoring of physical system based on trained bayesian neural network
M Kandemir, M Haussmann
US Patent App. 16/831,174, 2020
2020
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
M Haussmann, S Gerwinn, A Look, B Rakitsch, M Kandemir
arXiv preprint arXiv:2006.09914, 2020
2020
Supplementary Material for the Paper:” Variational Bayesian Multiple Instance Learning with Gaussian Processes”
M Hau▀mann, FA Hamprecht, M Kandemir
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