| 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 | 1983 | 2016 |
| 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 | 140 | 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 | 113 | 2010 |
| Learning hierarchical priors in vaes A Klushyn, N Chen, R Kurle, B Cseke, P van der Smagt Advances in neural information processing systems 32, 2019 | 112 | 2019 |
| Continual Learning with Bayesian Neural Networks for Non-Stationary Data R Kurle, B Cseke, A Klushyn, P van der Smagt, S Günnemann International Conference on Learning Representations, 2020 | 81 | 2020 |
| Approximate marginals in latent Gaussian models B Cseke, T Heskes The Journal of Machine Learning Research 12, 417-454, 2011 | 68 | 2011 |
| Bayesian source localization with the multivariate Laplace prior M Gerven, B Cseke, R Oostenveld, T Heskes Advances in neural information processing systems 22, 2009 | 56 | 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, 1-17, 2013 | 52 | 2013 |
| Constrained probabilistic movement primitives for robot trajectory adaptation F Frank, A Paraschos, P van der Smagt, B Cseke IEEE Transactions on Robotics 38 (4), 2276-2294, 2021 | 44 | 2021 |
| Latent matters: Learning deep state-space models A Klushyn, R Kurle, M Soelch, B Cseke, P van der Smagt Advances in Neural Information Processing Systems 34, 10234-10245, 2021 | 40 | 2021 |
| Efficient low-order approximation of first-passage time distributions D Schnoerr, B Cseke, R Grima, G Sanguinetti Physical Review Letters 119 (21), 210601, 2017 | 22 | 2017 |
| Properties of Bethe free energies and message passing in Gaussian models B Cseke, T Heskes Journal of Artificial Intelligence Research 41, 1-24, 2011 | 21 | 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 | 19 | 2016 |
| 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 | 19 | 2016 |
| Approximate inference in latent Gaussian-Markov models from continuous time observations B Cseke, M Opper, G Sanguinetti Advances in neural information processing systems 26, 2013 | 18 | 2013 |
| Improving posterior marginal approximations in latent Gaussian models B Cseke, T Heskes Proceedings of the Thirteenth International Conference on Artificial …, 2010 | 12 | 2010 |
| Bounds on the Bethe free energy for Gaussian networks B Cseke, T Heskes arXiv preprint arXiv:1206.3243, 2012 | 7 | 2012 |
| Local distance preserving auto-encoders using continuous knn graphs N Chen, P van der Smagt, B Cseke Topological, Algebraic and Geometric Learning Workshops 2022, 55-66, 2022 | 4 | 2022 |
| Kernel principal component ranking: Robust ranking on noisy data E Tsivtsivadze, B Cseke, TM Heskes Sl: Pascal Lecture Series, 2009 | 4 | 2009 |
| Increasing the generalisaton capacity of conditional vaes A Klushyn, N Chen, B Cseke, J Bayer, P van der Smagt Artificial Neural Networks and Machine Learning–ICANN 2019: Deep Learning …, 2019 | 2 | 2019 |
Tom HeskesProfessor of Computer Science and Artificial Intelligence, Radboud University NijmegenVerified email at science.ru.nl
