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Results: 160
Number of items: 160
  • Open Access
    van den Berg, R., Hasenclever, L., Tomczak, J. M., & Welling, M. (2018). Sylvester Normalizing Flows for Variational Inference. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 393-402). AUAI Press. http://auai.org/uai2018/proceedings/papers/156.pdf
  • Open Access
    Veeling, B. S., van den Berg, R., & Welling, M. (2018). Predictive Uncertainty through Quantization. (1 ed.) University of Amsterdam. https://arxiv.org/abs/1810.05500v1
  • Open Access
    Louizos, C., Shalit, U., Mooij, J., Sontag, D., Zemel, R., & Welling, M. (2018). Causal Effect Inference with Deep Latent-Variable Models. 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. 10, pp. 6447-6457). (Advances in Neural Information Processing Systems; Vol. 30). Neural Information Processing Systems. https://papers.nips.cc/paper/2017/file/94b5bde6de888ddf9cde6748ad2523d1-Paper.pdf
  • Open Access
    Tomczak, J. M., & Welling, M. (2018). VAE with a VampPrior. Proceedings of Machine Learning Research, 84, 1214-1223. https://arxiv.org/abs/1705.07120
  • 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
    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
    Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018). Modeling Relational Data with Graph Convolutional Networks. In A. Gangemi, R. Navigli, M.-E. Vidal, P. Hitzler, R. Troncy, L. Hollink, A. Tordai, & M. Alam (Eds.), The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018 : proceedings (pp. 593-607). (Lecture Notes in Computer Science; Vol. 10843). Springer. https://doi.org/10.1007/978-3-319-93417-4_38
  • Open Access
    Oh, C., Gavves, E., & Welling, M. (2018). BOCK: Bayesian Optimization with Cylindrical Kernels. Proceedings of Machine Learning Research, 80, 3868-3877. http://proceedings.mlr.press/v80/oh18a.html
  • 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
    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
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