Christian Donner
Christian Donner
Eidgenössische Technische Hochschule Zürich
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Cited by
Cited by
Efficient Bayesian inference of sigmoidal Gaussian Cox processes
C Donner, M Opper
The Journal of Machine Learning Research 19 (1), 2710-2743, 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
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
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
Inverse Ising problem in continuous time: A latent variable approach
C Donner, M Opper
Physical Review E 96 (6), 062104, 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
Efficient bayesian inference for a gaussian process density model
C Donner, M Opper
arXiv preprint arXiv:1805.11494, 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
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
Segmentation of sputum cell image for early lung cancer detection
N Werghi, C Donner, F Taher, H Alahmad
IET Digital Library, 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
Scalable logit gaussian process classification
F Wenzel, T Galy-Fajou, C Donner, M Kloft, M Opper
Advances in Approximate Bayesian Inference, NIPS Workshop, 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
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
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
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
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
Inferring the collective dynamics of neuronal populations from single-trial spike trains using mechanistic models
C Donner, M Opper, J Ladenbauer
bioRxiv, 671909, 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
Journal article: Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
C Donner, M Opper
Bayesian inference of inhomogeneous point process models 19, 11, 2019
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