| Efficient Bayesian inference of sigmoidal Gaussian Cox processes C Donner, M Opper Journal of Machine Learning Research 19 (67), 1-34, 2018 | 35 | 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 | 29 | 2017 |
| 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 | 28 | 2020 |
| 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 | 28 | 2013 |
| Inverse Ising problem in continuous time: A latent variable approach C Donner, M Opper Physical Review E 96 (6), 062104, 2017 | 21 | 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 | 17 | 2022 |
| Efficient Bayesian inference for a Gaussian process density model C Donner, M Opper arXiv preprint arXiv:1805.11494, 2018 | 13 | 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 | 10 | 2012 |
| Segmentation of sputum cell image for early lung cancer detection N Werghi, C Donner, F Taher, H Alahmad IET Digital Library, 2012 | 6 | 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 |
| 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 |
| Generative inverse design of multimodal resonant structures for locally resonant metamaterials S Dedoncker, C Donner, L Taenzer, B Van Damme arXiv preprint arXiv:2309.04177, 2023 | 1 | 2023 |
| Optimization of resonant absorbers for passive vibration control: a numerical approach and its experimental validation S Dedoncker, C Donner, L Taenzer, B Van Damme Available at SSRN 4266071, 2022 | 1 | 2022 |
| 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 | 1 | 2020 |
| Bayesian inference of inhomogeneous point process models C Donner PhD thesis, Technische Universität Berlin, 2019 | 1 | 2019 |
| 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–assisted platform for electrophysiological phenotyping of human neuronal networks P Hornauer, G Prack, N Anastasi, S Ronchi, T Kim, C Donner, M Fiscella, ... Stem Cell Reports 19 (2), 285-298, 2024 | | 2024 |
| Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains C Donner, J Bartram, P Hornauer, T Kim, D Roqueiro, A Hierlemann, ... bioRxiv, 2024.02. 01.578336, 2024 | | 2024 |
| A projected nonlinear state-space model for forecasting time series signals C Donner, A Mishra, H Shimazaki arXiv preprint arXiv:2311.13247, 2023 | | 2023 |
| Modelling heteroscedasticity in state-space models C Donner | | 2022 |
