Microfiltration of skim milk using polymeric membranes for casein concentrate manufacture

Autor: Lawrence, N.D., Kentish, S.E., O’Connor, A.J., Barber, A.R., Stevens, G.W.

Publikováno v: In Separation and Purification Technology 2008 60(3):237-244

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Variational inference for latent variables and uncertain inputs in Gaussian processes

Autor: Damianou, A.C., Titsias, M.K., Lawrence, N.D.

The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied. However, the current approach for training GP-LVMs is based on maximum likelihood, where the latent

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44a73023a5ad9ec16a7b828a423e0910
https://eprints.whiterose.ac.uk/103730/1/damianou16a.pdf

Practical and optimal LSH for angular distance

Autor: Andoni, Alexandr, Indyk, Piotr, Laarhoven, T.M.M., Razenshteyn, Ilya, Schmidt, Ludwig, Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R.

Publikováno v: Advances in Neural Information Processing Systems (NIPS, Montreal, Canada, December 7-10, 2015), 1225-1233
STARTPAGE=1225;ENDPAGE=1233;TITLE=Advances in Neural Information Processing Systems (NIPS, Montreal, Canada, December 7-10, 2015)

We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal running time exponent. Unlike earlier algorithms with this property

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::42c6522fd51119963bfc8c8dc879cf36
https://research.tue.nl/nl/publications/ca0892cd-40b6-45e6-a087-57b55f8b1ee7

Variational Dropout and the Local Reparameterization Trick

Autor: Kingma, D.P., Salimans, T., Welling, M., Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R.

Publikováno v: 29th Annual Conference on Neural Information Processing Systems 2015: Montreal, Canada, 7-12 December 2015, 3, 2575-2583

We investigate a local reparameterizaton technique for greatly reducing the variance of stochastic gradients for variational Bayesian inference (SGVB) of a posterior over model parameters, while retaining parallelizability. This local reparameterizat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::cd204a858c69bf0bee074a93866e6371
https://dare.uva.nl/personal/pure/en/publications/variational-dropout-and-the-local-reparameterization-trick(a1d34ae0-8b00-4571-9b3b-00b6599d349a).html