Search results
Results: 160
Number of items: 160
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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 -
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
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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
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Korattikara, A., Chen, Y., & Welling, M. (2014). Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. JMLR Workshop and Conference Proceedings, 32, 181-189. http://jmlr.org/proceedings/papers/v32/korattikara14.html
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Ahn, S., Shahbaba, B., & Welling, M. (2014). Distributed Stochastic Gradient MCMC. JMLR Workshop and Conference Proceedings, 32, 1044-1052. http://jmlr.org/proceedings/papers/v32/ahn14.html
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Kingma, D. P., & Welling, M. (2014). Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. JMLR Workshop and Conference Proceedings, 32, 1782-1790. http://jmlr.org/proceedings/papers/v32/kingma14.html
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Cohen, T., & Welling, M. (2014). Learning the Irreducible Representations of Commutative Lie Groups. JMLR Workshop and Conference Proceedings, 32, 1755-1763. http://jmlr.org/proceedings/papers/v32/cohen14.html
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Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Conference proceedings: papers accepted to the International Conference on Learning Representations (ICLR) 2014 ArXiv. http://arxiv.org/abs/1312.6114
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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
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Foulds, J., Boyles, L., DuBois, C., Smyth, P., & Welling, M. (2013). Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation. In I. S. Dhillon, Y. Koren, R. Ghani, T. E. Senator, P. Bradley, R. Parekh, J. He, R. L. Grossman, & R. Uthurusamy (Eds.), KDD '13: the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: August 11-14, 2013, Chicago, Illinois, USA (pp. 446-454). ACM. https://doi.org/10.1145/2487575.2487697
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