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Results: 5
Number of items: 5
  • Open Access
    Ullrich, K. (2020). A coding perspective on deep latent variable models. [Thesis, fully internal, Universiteit van Amsterdam].
  • 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
  • Horn, C., Metzler, P., Ullrich, K., Koschorreck, M., & Boehrer, B. (2017). Methane storage and ebullition in monimolimnetic waters of polluted mine pit lake Vollert-Sued, Germany. Science of the Total Environment, 584–585, 1-10. https://doi.org/10.1016/j.scitotenv.2017.01.151
  • Open Access
    van der Wel, E., & Ullrich, K. (2017). Optical Music Recognition with Convolutional Sequence-to-Sequence Models. In X. Hu, S. J. Cunningham, D. Turnbull, & Z. Duan (Eds.), ISMIR 2017: Proceedings of the 18th International Society for Music Information Retrieval Conference : October 23-27, 2017, Suzhou, China (pp. 731-737). ISMIR. https://doi.org/10.5281/zenodo.1415664
  • 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
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