| Efficient Bayesian inference of sigmoidal Gaussian Cox processes C Donner, M Opper The Journal of Machine Learning Research 19 (1), 2710-2743, 2018 | 31 | 2018 |
| Approximate inference for time-varying interactions and macroscopic dynamics of neural populations C Donner, K Obermayer, H Shimazaki PLoS computational biology 13 (1), e1005309, 2017 | 28 | 2017 |
| Extraction and segmentation of sputum cells for lung cancer early diagnosis F Taher, N Werghi, H Al-Ahmad, C Donner Algorithms 6 (3), 512-531, 2013 | 22 | 2013 |
| Multi-class gaussian process classification made conjugate: Efficient inference via data augmentation T Galy-Fajou, F Wenzel, C Donner, M Opper Uncertainty in Artificial Intelligence, 755-765, 2020 | 21 | 2020 |
| Inverse Ising problem in continuous time: A latent variable approach C Donner, M Opper Physical Review E 96 (6), 062104, 2017 | 20 | 2017 |
| GP-ETAS: semiparametric Bayesian inference for the spatio-temporal epidemic type aftershock sequence model C Molkenthin, C Donner, S Reich, G Zöller, S Hainzl, M Holschneider, ... Statistics and computing 32 (2), 29, 2022 | 11 | 2022 |
| Efficient bayesian inference for a gaussian process density model C Donner, M Opper arXiv preprint arXiv:1805.11494, 2018 | 11 | 2018 |
| Detection and segmentation of sputum cell for early lung cancer detection N Werghi, C Donner, F Taher, H Al-Ahmad 2012 19th IEEE International Conference on Image Processing, 2813-2816, 2012 | 9 | 2012 |
| Cell extraction from sputum images for early lung cancer detection C Donner, N Werghi, F Taher, H Al-Ahmad 2012 16th IEEE Mediterranean Electrotechnical Conference, 485-488, 2012 | 4 | 2012 |
| Segmentation of sputum cell image for early lung cancer detection N Werghi, C Donner, F Taher, H Alahmad IET Digital Library, 2012 | 3 | 2012 |
| Scalable multi-class Gaussian process classification via data augmentation T Galy-Fajou, F Wenzel, C Donner, M Opper Proc. NIPS Workshop Approx. Inference, 1-12, 2018 | 2 | 2018 |
| Scalable logit gaussian process classification F Wenzel, T Galy-Fajou, C Donner, M Kloft, M Opper Advances in Approximate Bayesian Inference, NIPS Workshop, 2017 | 1 | 2017 |
| DeePhys, a machine learning-driven platform for electrophysiological phenotype screening of human stem-cell derived neuronal networks P Hornauer, G Prack, N Anastasi, S Ronchi, T Kim, C Donner, M Fiscella, ... MEA Meeting 2022 Abstract Book, 158-160, 2022 | | 2022 |
| Downregulating α-synuclein in iPSC-derived dopaminergic neurons mimics electrophysiological phenotype of the A53T mutation P Hornauer, G Prack, N Anastasia, S Ronchi, T Kim, C Donner, M Fiscella, ... bioRxiv, 2022.03. 31.486582, 2022 | | 2022 |
| Learning interpretable latent dynamics for a 2D airfoil system C Donner, N Tagasovska, G He, K Mulleners, H Shimazaki, G Obozinski RobustML workshop at ICLR 2021, 2021 | | 2021 |
| Comparison of connectivity inference algorithms for classification of neuronal cultures using graph kernels T Kim, P Hornauer, C Donner, A Hierlemann, K Borgwardt, M Schröter, ... ECML PKDD Workshop on Machine Learning for Pharma and Healthcare …, 2020 | | 2020 |
| Electrophysiological characterization of α-synuclein function in human iPSC-derived dopaminergic neurons using high-density microelectrode arrays P Hornauer, G Prack, M Fiscella, C Donner, DS Roqueiro, V Taylor, ... ISSCR 2020 Poster Abstract Guide, MDD448, 2020 | | 2020 |
| Inferring the collective dynamics of neuronal populations from single-trial spike trains using mechanistic models C Donner, M Opper, J Ladenbauer bioRxiv, 671909, 2019 | | 2019 |
| Conference article: Efficient Bayesian Inference for a Gaussian Process Density Model C Donner, M Opper Bayesian inference of inhomogeneous point process models, 47, 2019 | | 2019 |
| Journal article: Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes C Donner, M Opper Bayesian inference of inhomogeneous point process models 19, 11, 2019 | | 2019 |
