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Results: 154
Number of items: 154
  • Open Access
    Kipf, T., Fetaya, E., Wang, K.-C., Welling, M., & Zemel, R. (2018). Neural Relational Inference for Interacting Systems. Proceedings of Machine Learning Research, 80, 2688-2697. http://proceedings.mlr.press/v80/kipf18a.html
  • Open Access
    Ilse, M., Tomczak, J. M., & Welling, M. (2018). Attention-based Deep Multiple Instance Learning. Proceedings of Machine Learning Research, 80, 2127-2136. http://proceedings.mlr.press/v80/ilse18a.html
  • Open Access
    Louizos, C., Ullrich, K., & Welling, M. (2018). Bayesian Compression for Deep Learning. In U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), 31st Conference on Advances in Neural Information Processing Systems (NIPS 2017): Long Beach, California, USA, 4-9 December 2017 (Vol. 5, pp. 3289-3299). (Advances in Neural Information Processing Systems; Vol. 30). Neural Information Processing Systems. https://papers.nips.cc/paper/6921-bayesian-compression-for-deep-learning
  • Open Access
    Eck, A., de Groot, E. F. J., de Meij, T. G. J., Welling, M., Savelkoul, P. H. M., & Budding, A. E. (2017). Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease. Journal of Clinical Microbiology, 55(6), 1720-1732. https://doi.org/10.1128/JCM.00162-17
  • 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
    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
    Federici, M., Ullrich, K., & Welling, M. (2017). Improved Bayesian Compression. Paper presented at Bayesian Deep Learning Workshop NIPS 2017, Long Beach, United States. http://bayesiandeeplearning.org/2017/papers/16.pdf
  • 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
    van den Berg, R., Kipf, T. N., & Welling, M. (2017). Graph Convolutional Matrix Completion. ArXiv. https://arxiv.org/abs/1706.02263
  • 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
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