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Results: 154
Number of items: 154
  • Meeds, E., Leenders, R., & Welling, M. (2015). Hamiltonian ABC. In M. Meila, & T. Heskes (Eds.), Uncertainty in Artificial Intelligence: proceedings of the thirty-first conference (2015): July 12-16, Amsterdam, Netherlands (pp. 582-591). AUAI Press. http://auai.org/uai2015/proceedings/papers/266.pdf
  • Open Access
    Meeds, E., & Welling, M. (2015). Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. 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. 3, pp. 2080-2088). (Advances in Neural Information Processing Systems; Vol. 28). Curran Associates. http://papers.nips.cc/paper/5881-optimization-monte-carlo-efficient-and-embarrassingly-parallel-likelihood-free-inference
  • Open Access
    Cohen, T. S., & Welling, M. (2015). Harmonic Exponential Families on Manifolds. JMLR Workshop and Conference Proceedings, 37, 1757-1765. http://jmlr.org/proceedings/papers/v37/cohenb15.html
  • Open Access
    Chiang, M., Cinquin, A., Paz, A., Meeds, E., Price, C. A., Welling, M., & Cinquin, O. (2015). Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation. BMC Biology, 13, Article 51. https://doi.org/10.1186/s12915-015-0148-y
  • Open Access
    Meeds, E., Chiang, M., Lee, M., Cinquin, O., Lowengrub, J., & Welling, M. (2015). POPE: Post Optimization Posterior Evaluation of Likelihood Free Models. BMC Bioinformatics, 16, Article 264. https://doi.org/10.1186/s12859-015-0658-1
  • 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
  • Open Access
    Meeds, E., Hendriks, R., Al Faraby, S., Bruntink, M., & Welling, M. (2015). MLitB: Machine Learning in the Browser. PeerJ Computer Science, 1, Article e11. https://doi.org/10.7717/peerj-cs.11
  • Open Access
    Salimans, T., Kingma, D. P., & Welling, M. (2015). Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. JMLR Workshop and Conference Proceedings, 37, 1218-1226. http://jmlr.org/proceedings/papers/v37/salimans15.html
  • Open Access
    Kingma, D. P., Salimans, T., & Welling, M. (2015). Variational Dropout and the Local Reparameterization Trick. 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. 3, pp. 2575-2583). (Advances in Neural Information Processing Systems; Vol. 28). Curran Associates. http://papers.nips.cc/paper/5666-variational-dropout-and-the-local-reparameterization-trick
  • 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
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