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Results: 156
Number of items: 156
  • Open Access
    Cohen, T. S., & Welling, M. (2015). Transformation Properties of Learned Visual Representations. In ICLR 2015: accepted papers - Main Conference - Poster Presentations ArXiv. http://arxiv.org/abs/1412.7659
  • Open Access
    Korattikara, A., Rathod, V., Murphy, K., & Welling, M. (2015). Bayesian Dark Knowledge. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), 29th Annual Conference on Neural Information Processing Systems 2015: Montreal, Canada, 7-12 December 2015 (Vol. 4, pp. 3438-3446). (Advances in Neural Information Processing Systems; Vol. 28). Curran Associates. http://papers.nips.cc/paper/5965-bayesian-dark-knowledge
  • Open Access
    Ahn, S., Korattikara, A., Liu, N., Rajan, S., & Welling, M. (2015). Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC. In KDD'15: proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: August 10-13, 2015, Sydney, Australia (pp. 9-18). Association for Computing Machinery. https://doi.org/10.1145/2783258.2783373
  • Chen, Y., Gelfand, A. E., & Welling, M. (2014). Herding for Structured Prediction. In S. Nowozin, P. V. Gehler, J. Jancsary, & C. H. Lampert (Eds.), Advanced structured prediction (pp. 187-212). (Neural information processing series). The MIT Press. https://mitpress.mit.edu/books/advanced-structured-prediction
  • Burges, C. J. C., Bottou, L., Welling, M., Ghahramani, Z., & Weinberger, K. Q. (2014). 27th Annual Conference on Neural Information Processing Systems 2013: December 5-10, Lake Tahoe, Nevada, USA. (Advances in Neural Information Processing Systems; Vol. 26). Curran. http://papers.nips.cc/book/advances-in-neural-information-processing-systems-26-2013
  • Meeds, E., & Welling, M. (2014). GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. In N. Zhang, & J. Tian (Eds.), Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence: Quebec City, Quebec, Canada: July 23-27, 2014: UAI2014 (pp. 593-602). AUAI Press. http://auai.org//uai2014/proceedings/uai-2014-proceedings.pdf
  • Open Access
    Meeds, E., Hendriks, R., al Faraby, S., Bruntink, M., & Welling, M. (2014). MLitB: Machine Learning in the Browser. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.1412.2432
  • Open Access
    Salimans, T., Kingma, D. P., & Welling, M. (2014). Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. In Accepted papers: Advances in Variational Inference: NIPS 2014 Workshop: 13 December 2014, Convention and Exhibition Center, Montreal, Canada NIPS Foundation. https://drive.google.com/file/d/0BwY-r_90KHY4d3ZTNDJpY3FYRS1rVEVVb3lUQzMzdk01Q2VV/view?pli=1
  • Open Access
    DuBois, C., Korattikara, A., Welling, M., & Smyth, P. (2014). Approximate Slice Sampling for Bayesian Posterior Inference. JMLR Workshop and Conference Proceedings, 33, 185-193. http://jmlr.org/proceedings/papers/v33/dubois14.html
  • Open Access
    Welling, M. (2014). Van veel data, snelle computers en complexe modellen tot lerende machines. (Oratiereeks). Universiteit van Amsterdam. http://www.oratiereeks.nl/upload/pdf/PDF-8573weboratie_Welling_HR.pdf
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