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Results: 157
Number of items: 157
  • Open Access
    Park, M., Foulds, J., Chaudhuri, K., & Welling, M. (2017). DP-EM: Differentially Private Expectation Maximization. Proceedings of Machine Learning Research, 54, 896-904. http://proceedings.mlr.press/v54/park17c.html
  • Open Access
    Kingma, D., Salimans, T., Josefowicz, R., Chen, X., Sutskever, I., & Welling, M. (2017). Improving Variational Autoencoders with Inverse Autoregressive Flow. In D. D. Lee, U. von Luxburg, R. Garnett, M. Sugiyama, & I. Guyon (Eds.), 30th Annual Conference on Neural Information Processing Systems 2016: Barcelona, Spain, 5-10 December 2016 (Vol. 7, pp. 4743-4751). (Advances in Neural Information Processing Systems; Vol. 29). Curran Associates. https://arxiv.org/abs/1606.04934
  • Open Access
    Tomczak, J. M., Ilse, M., & Welling, M. (2017). Deep Learning with Order-invariant Operator for Multi-instance Histopathology Classification. Abstract from Medical Imaging meets NIPS Workshop NIPS 2017, Long Beach, United States. https://doi.org/10.48550/arXiv.1712.00310
  • Open Access
    Louizos, C., & Welling, M. (2017). Multiplicative Normalizing Flows for Variational Bayesian Neural Networks. Proceedings of Machine Learning Research, 70, 2218-2227. http://proceedings.mlr.press/v70/louizos17a.html
  • Open Access
    Adel, T., Cohen, T., Caan, M., Welling, M., AGEhIV Study Group, & Alzheimer's Disease Neuroimaging Initiative (2017). 3D scattering transforms for disease classification in neuroimaging. NeuroImage: Clinical, 14, 506-517. https://doi.org/10.1016/j.nicl.2017.02.004
  • Open Access
    Eck, A., Zintgraf, L. M., de Groot, E. F. J., de Meij, T. G. J., Cohen, T. S., Savelkoul, P. H. M., Welling, M., & Budding, A. E. (2017). Interpretation of microbiota-based diagnostics by explaining individual classifier decisions. BMC Bioinformatics, 18, Article 441. https://doi.org/10.1186/s12859-017-1843-1
  • Open Access
    Hasenclever, L., Tomczak, J. M., van den Berg, R., & Welling, M. (2017). Variational Inference with Orthogonal Normalizing Flows. Paper presented at Bayesian Deep Learning Workshop NIPS 2017, Long Beach, United States. http://bayesiandeeplearning.org/2017/papers/51.pdf
  • Chen, Y., & Welling, M. (2016). Herding as a Learning System with Edge-of-Chaos Dynamics. In T. Hazan, G. Papandreou, & D. Tarlow (Eds.), Perturbations, Optimization, and Statistics (pp. 73-125). (Neural Information Processing series). The MIT Press. https://doi.org/10.7551/mitpress/10761.003.0005
  • El-Helw, I., Hofman, R., Li, W., Ahn, S., Welling, M., & Bal, H. (2016). Scalable Overlapping Community Detection. In 2016 IEEE 30th International Parallel and Distributed Processing Symposium Workshops : IPDPSW 2016: proceedings : 23-27 May 2016, Chicago, Illinois (pp. 1463-1472). IEEE Computer Society. https://doi.org/10.1109/IPDPSW.2016.165
  • Open Access
    Louizos, C., & Welling, M. (2016). Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors. JMLR Workshop and Conference Proceedings, 48, 1708-1716. http://proceedings.mlr.press/v48/louizos16.html
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