| Deep-learning jets with uncertainties and more S Bollweg, M Haußmann, G Kasieczka, M Luchmann, T Plehn, ... SciPost Phys 8 (006), 1904.10004, 2020 | 17 | 2020 |
| Variational Bayesian Multiple Instance Learning with Gaussian Processes M Haußmann, FA Hamprecht, M Kandemir The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6570-6579, 2017 | 12 | 2017 |
| Deep active learning with adaptive acquisition M Haußmann, FA Hamprecht, M Kandemir arXiv preprint arXiv:1906.11471, 2019 | 9 | 2019 |
| Sampling-free variational inference of bayesian neural networks by variance backpropagation M Haußmann, 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 | 7 | 2016 |
| LeMoNADe: learned motif and neuronal assembly detection in calcium imaging videos E Kirschbaum, M Haußmann, S Wolf, H Sonntag, J Schneider, S Elzoheiry, ... arXiv preprint arXiv:1806.09963, 2018 | 3 | 2018 |
| 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 Haußmann, FA Hamprecht, M Kandemir | | |
Melih KandemirGroup Leader at BCAI, Adjunct Professor at ITUVerified email at de.bosch.com
