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Results: 154
Number of items: 154
  • Open Access
    Kipf, T. N., & Welling, M. (2016). Variational Graph Auto-Encoders. Paper presented at Bayesian Deep Learning Workshop NIPS 2016, Barcelona, Spain. https://doi.org/10.48550/arXiv.1611.07308
  • Open Access
    Park, M., Foulds, J., Chaudhuri, K., & Welling, M. (2016). Private Topic Modeling. In Private Multi-Party Machine Learning: NIPS 2016 workshop : Barcelona, December 9 : PMPML'16 NIPS. https://arxiv.org/abs/1609.04120
  • Open Access
    Tomczak, J. M., & Welling, M. (2016). Improving Variational Auto-Encoders using Householder Flow. Paper presented at Bayesian Deep Learning Workshop NIPS 2016, Barcelona, Spain. https://arxiv.org/abs/1611.09630
  • Open Access
    Foulds, J., Geumlek, J., Welling, M., & Chaudhuri, K. R. (2016). On the Theory and Practice of Privacy Preserving Data Analysis. In A. Ihler, & D. Janzing (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Second Conference (2016) : June 25-29, 2016, Jersey City, New Jersey, USA (pp. 192-201). Article 45 AUAI Press. http://www.auai.org/uai2016/proceedings/papers/45.pdf
  • Open Access
    Welling, M. (2016). Marrying Graphical Models with Deep Learning. ERCIM News, 107, 20-21. https://ercim-news.ercim.eu/en107
  • Open Access
    Louizos, C., Swersky, K., Li, Y., Welling, M., & Zemel, R. (2016). The Variational Fair Autoencoder. In ICLR 2016: International Conference on Learning Representations: May 2-4, 2016, San Juan, Puerto Rico. Accepted papers (Conference Track) Computational and Biological Learning Society. https://arxiv.org/abs/1511.00830
  • Open Access
    Korattikara, A., Chen, Y., & Welling, M. (2016). Sequential Tests for Large Scale Learning. Neural Computation, 28(1), 45-70. https://doi.org/10.1162/NECO_a_00226
  • Open Access
    Cohen, T. S., & Welling, M. (2016). Group Equivariant Convolutional Networks. JMLR Workshop and Conference Proceedings, 48, 2990-2999. http://proceedings.mlr.press/v48/cohenc16.html
  • Open Access
    Roijers, D. M. (2016). Multi-objective decision-theoretic planning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Kingma, D. P., Rezende, D. J., Mohamed, S., & Welling, M. (2015). Semi-supervised Learning with Deep Generative Models. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), 28th Annual Conference on Neural Information Processing Systems 2014: December 8-13, 2014, Montreal, Canada (Vol. 4, pp. 3581-3589). (Advances in Neural Information Processing Systems; Vol. 27). Curran. http://papers.nips.cc/paper/5352-semi-supervised-learning-with-deep-generative-models
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