Search results
Results: 157
Number of items: 157
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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 -
Chen, Y., Bornn, L., de Freitas, N., Eskelin, M., Fang, J., & Welling, M. (2016). Herded Gibbs Sampling. Journal of Machine Learning Research, 17, Article 10. http://www.jmlr.org/papers/v17/chen16a.html
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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
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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
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Welling, M. (2016). Marrying Graphical Models with Deep Learning. ERCIM News, 107, 20-21. https://ercim-news.ercim.eu/en107
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O'Connor, P., & Welling, M. (2016). Deep Spiking Networks. ArXiv. https://arxiv.org/abs/1602.08323v2
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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
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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
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Li, W., Ahn, S., & Welling, M. (2016). Scalable MCMC for Mixed Membership Stochastic Blockmodels. JMLR Workshop and Conference Proceedings, 51, 723-731. http://jmlr.org/proceedings/papers/v51/li16d.html
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