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Logo DIANNE 0.5.0

by sbohez - October 25, 2016, 19:51:07 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 75 views, 7 downloads, 1 subscription

About: DIANNE is a modular software framework for designing, training and evaluating artificial neural networks on heterogeneous, distributed infrastructure . It is built on top of OSGi and AIOLOS and can transparently deploy and redeploy (parts of) a neural network on multiple machines, as well as scale up training on a compute cluster.


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Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 4.0

by hn - October 19, 2016, 10:15:05 CET [ Project Homepage BibTeX Download ] 37235 views, 8433 downloads, 4 subscriptions

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About: The GPML toolbox is a flexible and generic Octave/Matlab implementation of inference and prediction with Gaussian process models. The toolbox offers exact inference, approximate inference for non-Gaussian likelihoods (Laplace's Method, Expectation Propagation, Variational Bayes) as well for large datasets (FITC, VFE, KISS-GP). A wide range of covariance, likelihood, mean and hyperprior functions allows to create very complex GP models.


A major code restructuring effort did take place in the current release unifying certain inference functions and allowing more flexibility in covariance function composition. We also redesigned the whole derivative computation pipeline to strongly improve the overall runtime. We finally include grid-based covariance approximations natively.

More generic sparse approximation using Power EP

  • unified treatment of FITC approximation, variational approaches VFE and hybrids

  • inducing input optimisation for all (compositions of) covariance functions dropping the previous limitation to a few standard examples

  • infFITC is now covered by the more generic infGaussLik function

Approximate covariance object unifying sparse approximations, grid-based approximations and exact covariance computations

  • implementation in cov/apx, cov/apxGrid, cov/apxSparse

  • generic infGaussLik unifies infExact, infFITC and infGrid

  • generic infLaplace unifies infLaplace, infFITC_Laplace and infGrid_Laplace

Hiearchical structure of covariance functions

  • clear hierachical compositional implementation

  • no more code duplication as present in covSEiso and covSEard pairs

  • two mother covariance functions

    • covDot for dot-product-based covariances and

    • covMaha for Mahalanobis-distance-based covariances

  • a variety of modifiers: eye, iso, ard, proj, fact, vlen

  • more flexibility as more variants are available and possible

  • all covariance functions offer derivatives w.r.t. inputs

Faster derivative computations for mean and cov functions

  • switched from partial derivatives to directional derivatives

  • simpler and more concise interface of mean and cov functions

  • much faster marginal likelihood derivative computations

  • simpler and more compact code

New mean functions

  • new mean/meanWSPC (Weighted Sum of Projected Cosines or Random Kitchen Sink features) following a suggestion by William Herlands

  • new mean/meanWarp for constructing a new mean from an existing one by means of a warping function adapted from William Herlands

New optimizer

  • added a new minimize_minfunc, contributed by Truong X. Nghiem

New GLM link function

  • added the twice logistic link function util/glm_invlink_logistic2

Smaller fixes

  • two-fold speedup of util/elsympol used by covADD by Truong X. Nghiem

  • bugfix in util/logphi as reported by John Darby

Logo python weka wrapper 0.3.9

by fracpete - October 18, 2016, 22:55:00 CET [ Project Homepage BibTeX Download ] 34716 views, 6853 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

  • plot_learning_curve method of module weka.plot.classifiers now accepts a list of test sets; * is index of test set in label template string
  • added missing_value() methods to weka.core.dataset module and Instance class
  • output variable y for convenience method create_instances_from_lists in module weka.core.dataset is now optional
  • added convenience method create_instances_from_matrices to weka.core.dataset module to easily create an Instances object from numpy matrices (x and y)

Logo AMIDST Toolbox 0.6.0

by ana - October 14, 2016, 19:35:27 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4365 views, 668 downloads, 4 subscriptions

About: A Java Toolbox for Scalable Probabilistic Machine Learning.

  • Added sparklink module implementing the integration with Apache Spark. More information here.
  • Fluent pattern in latent-variable-models
  • Predefined model implementing the concept drift detection

Detailed information can be found in the toolbox's web page

Logo revrand 0.7.0

by dsteinberg - October 14, 2016, 08:31:02 CET [ Project Homepage BibTeX Download ] 6606 views, 1238 downloads, 3 subscriptions

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

  • Ability to set the random state in all random basis functions, optimisers and the generalised linear model
  • Numerous numerical bug fixes
  • small performance optimisations

Logo JMLR dlib ml 19.2

by davis685 - October 11, 2016, 01:54:09 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 157983 views, 25399 downloads, 5 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.


This release adds a number of new features, most important of which is a deep convolutional neural network version of the max-margin object detection algorithm. This tool makes it very easy to create high quality object detectors. See for an introduction.

Logo Somoclu 1.7.1

by peterwittek - October 2, 2016, 10:48:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 20020 views, 3675 downloads, 3 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, Julia, R, and MATLAB are supported.

  • Fixed: macOS build works again.

Logo r-cran-CoxBoost 1.4

by r-cran-robot - October 1, 2016, 00:00:04 CET [ Project Homepage BibTeX Download ] 29117 views, 5676 downloads, 3 subscriptions

About: Cox models by likelihood based boosting for a single survival endpoint or competing risks


Fetched by r-cran-robot on 2016-10-01 00:00:04.178988

Logo r-cran-e1071 1.6-7

by r-cran-robot - October 1, 2016, 00:00:04 CET [ Project Homepage BibTeX Download ] 30374 views, 6230 downloads, 3 subscriptions

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About: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly


Fetched by r-cran-robot on 2016-10-01 00:00:04.307859

Logo r-cran-Boruta 5.1.0

by r-cran-robot - October 1, 2016, 00:00:03 CET [ Project Homepage BibTeX Download ] 20445 views, 4249 downloads, 2 subscriptions

About: Wrapper Algorithm for All Relevant Feature Selection


Fetched by r-cran-robot on 2016-10-01 00:00:03.742650

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