Botond Cseke
Botond Cseke
Volkswagen Data Lab
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Cited by
f-gan: Training generative neural samplers using variational divergence minimization
S Nowozin, B Cseke, R Tomioka
Advances in Neural Information Processing Systems, 271-279, 2016
Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior
MAJ Van Gerven, B Cseke, FP De Lange, T Heskes
NeuroImage 50 (1), 150-161, 2010
Network of epistatic interactions within a yeast snoRNA
O Puchta, B Cseke, H Czaja, D Tollervey, G Sanguinetti, G Kudla
Science 352 (6287), 840-844, 2016
Approximate marginals in latent Gaussian models
B Cseke, T Heskes
The Journal of Machine Learning Research 12, 417-454, 2011
Bayesian source localization with the multivariate Laplace prior
MAJ van Gerven, B Cseke, R Oostenveld, TM Heskes
[Sl: sn], 2009
MMDiff: quantitative testing for shape changes in ChIP-Seq data sets
G Schweikert, B Cseke, T Clouaire, A Bird, G Sanguinetti
BMC genomics 14 (1), 1-17, 2013
Learning hierarchical priors in VAEs
A Klushyn, N Chen, R Kurle, B Cseke, P van der Smagt
arXiv preprint arXiv:1905.04982, 2019
Approximate inference in latent Gaussian-Markov models from continuous time observations.
B Cseke, M Opper, G Sanguinetti
NIPS, 971-979, 2013
Properties of Bethe free energies and message passing in Gaussian models
B Cseke, T Heskes
Journal of Artificial Intelligence Research 41, 1-24, 2011
Expectation propagation for continuous time stochastic processes
B Cseke, D Schnoerr, M Opper, G Sanguinetti
Journal of Physics A: Mathematical and Theoretical 49 (49), 494002, 2016
Efficient low-order approximation of first-passage time distributions
D Schnoerr, B Cseke, R Grima, G Sanguinetti
Physical Review Letters 119 (21), 210601, 2017
Sparse approximate inference for spatio-temporal point process models
B Cseke, A Zammit-Mangion, T Heskes, G Sanguinetti
Journal of the American Statistical Association 111 (516), 1746-1763, 2016
Improving posterior marginal approximations in latent Gaussian models
B Cseke, T Heskes
Proceedings of the Thirteenth International Conference on Artificial†…, 2010
Continual Learning with Bayesian Neural Networks for Non-Stationary Data
R Kurle, B Cseke, A Klushyn, P van der Smagt, S GŁnnemann
International Conference on Learning Representations, 2020
Bounds on the Bethe free energy for Gaussian networks
B Cseke, T Heskes
arXiv preprint arXiv:1206.3243, 2012
Kernel principal component ranking: Robust ranking on noisy data
E Tsivtsivadze, B Cseke, TM Heskes
[Sl]: Pascal Lecture Series, 2009
Increasing the generalisaton capacity of conditional vaes
A Klushyn, N Chen, B Cseke, J Bayer, P van der Smagt
International Conference on Artificial Neural Networks, 779-791, 2019
Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation
F Frank, A Paraschos, P van der Smagt, B Cseke
arXiv preprint arXiv:2101.12561, 2021
Factored expectation propagation for input-output FHMM models in systems biology
B Cseke, G Sanguinetti
arXiv preprint arXiv:1305.4153, 2013
Variational algorithms for Bayesian inference in latent Gaussian models
B Cseke
[Sl: sn], 2011
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