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B

Haußmann, M, Gerwinn, S and Kandemir, M (2019). Bayesian Prior Networks with PAC Training. arXiv preprint arXiv:1906.00816

D

Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Deep Active Learning with Adaptive Acquisition. IJCAI. Proceedings. 2470-2476

Technical Report (137.6 KB)

Bollweg, S, Haußmann, M, Kasieczka, G, Luchmann, M, Plehn, T and Thompson, J (2020). Deep-Learning Jets with Uncertainties and More. SciPost Phys. 8. https://scipost.org/10.21468/SciPostPhys.8.1.006

Technical Report (1.65 MB)

L

Kirschbaum, E, Haußmann, M, Wolf, S, Sonntag, H, Schneider, J, Elzoheiry, S, Kann, O, Durstewitz, D and Hamprecht, F A (2019). LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. ICLR. Proceedings

S

Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI. Proceedings

Technical Report (1.04 MB)

V

Haußmann, M, Hamprecht, F A and Kandemir, M (2017). Variational Bayesian Multiple Instance Learning with Gaussian Processes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6570-6579

Technical Report (1.29 MB)

Kandemir, M, Haußmann, M, Diego, F, Rajamani, K, van der Laak, J and Hamprecht, F A (2016). Variational weakly-supervised Gaussian processes. BMVC. Proceedings

Technical Report (3.28 MB)

W

Haußmann, (2016). Weakly Supervised Detection With Gaussian Processes. University of Heidelberg