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Results: 160
Number of items: 160
  • Open Access
    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
  • Open Access
    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
  • Open Access
    Welling, M. (2016). Marrying Graphical Models with Deep Learning. ERCIM News, 107, 20-21. https://ercim-news.ercim.eu/en107
  • Open Access
    O'Connor, P., & Welling, M. (2016). Deep Spiking Networks. ArXiv. https://arxiv.org/abs/1602.08323v2
  • 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
  • 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
  • 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
    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
    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
  • 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
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