Projects authored by daniel steinberg.


Logo Aboleth 0.6.2

by dsteinberg - October 13, 2017, 01:21:35 CET [ Project Homepage BibTeX Download ] 1417 views, 417 downloads, 3 subscriptions

About: A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation

Changes:

Hotfix release

  • fix random seeds
  • fix dropout sampling layers

Logo revrand 1.0.0

by dsteinberg - January 29, 2017, 04:33:54 CET [ Project Homepage BibTeX Download ] 15741 views, 3300 downloads, 3 subscriptions

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About: A library of scalable Bayesian generalised linear models with fancy features

Changes:
  • 1.0 release!
  • Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation
  • Regression regularizers (weight variances) associated with each basis object, this approximates GP kernel addition more closely
  • Random state can be set for all random objects
  • Numerous small improvements to make revrand production ready
  • Final report
  • Documentation improvements

Logo libcluster 2.3

by dsteinberg - February 27, 2016, 00:36:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7229 views, 1463 downloads, 3 subscriptions

About: An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.

Changes:

New maximum cluster argument for all algorithms. Also no more matlab interface since it seemed no one was using it, and I cannot support it any longer.


Logo linearizedGP 1.0

by dsteinberg - November 28, 2014, 07:02:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3389 views, 700 downloads, 1 subscription

About: Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation.

Changes:

Initial Announcement on mloss.org.