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V

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

Technical Report (3.28 MB)

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

Technical Report (1.29 MB)

L

M. Haußmann, Gerwinn, S., Look, A., Rakitsch, B., and Kandemir, M., “Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes”, International Conference on Artificial Intelligence and Statistics , vol. PMLR 130. pp. 478-486, 2021.

G

M. von Borstel, Kandemir, M., Schmidt, P., Rao, M., Rajamani, K., and Hamprecht, F. A., “Gaussian process density counting from weak supervision”, ECCV. Proceedings, vol. LNCS 9905. Springer, pp. 365-380 , 2016.

Technical Report (1.71 MB)

E

M. Kandemir, Agkül, A., Haußmann, M., and Ünal, G., “Evidential Turing Processes”. arXiv preprint, 2021.

M. Kandemir, Rubio, J. C., Schmidt, U., Wojek, C., Welbl, J., Ommer, B., and Hamprecht, F. A., “Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures”, in Medical Image Computing and Computer-Assisted Intervention, 2014, p. 154--161.

Technical Report (2 MB)

D

M. Kandemir and Hamprecht, F. A., “The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors”, NIPS. Proceedings, vol. 44. pp. 145-159, 2015.

Supplementary Material (223.39 KB)

Technical Report (2.58 MB)

B

M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Evidential Deep Learning with PAC Regularization ”, 3rd Symposium on Advances in Approximate Bayesian Inference . 2020.