About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. Changes:This release adds a deep learning toolkit to dlib that has a clean and fully documented C++11 API. It also includes CPU and GPU support, binds to cuDNN, can train on multiple GPUs at a time, and comes with a pretrained imagenet model based on ResNet34. The release also adds a number of other improvements such as new elastic net regularized solvers and QP solvers, improved MATLAB binding tools, and other usability tweaks and optimizations.

About: A java library for the analysis of data streams with probabilistic graphical models. AMIDST provides parallel multicore implementations of Bayesian parameter learning algorithms using variational message passing and importance sampling for static and dynamic Bayesian networks. Additionally, AMIDST efficiently leverages existing functionalities and algorithms by interfacing to software tools such as Weka, Moa, HUGIN and R. Changes:

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.

About: The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, sumproduct networks, arithmetic circuits, and mixtures of trees. Changes:Version 1.1.2d (12/29/2015):

About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields. Changes:Release 0.3.2 fixes various bugs and adds GLC (Generalized Loop Corrections) written by Siamak Ravanbakhsh.

About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models. Changes:

About: Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation. Changes:Initial Announcement on mloss.org.

About: The glmie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glmie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference. Changes:added factorial mean field inference as a third algorithm complementing expectation propagation and variational Bayes generalised nonGaussian potentials so that affine instead of linear functions of the latent variables can be used

About: Dataefficient policy search framework using probabilistic Gaussian process models Changes:Initial Announcement on mloss.org.

About: Gaussian process RTS smoothing (forwardbackward smoothing) based on moment matching. Changes:Initial Announcement on mloss.org.

About: Bayesian Reasoning and Machine Learning toolbox Changes:Fixed some small bugs and updated some demos.

About: Matlab implementation of variational gaussian approximate inference for Bayesian Generalized Linear Models. Changes:Code restructure and bug fix.

About: The library is focused on implementation of propagation based approximate inference methods. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm. Changes:Initial Announcement on mloss.org.

About: stroll (STRuctured Output Learning Library) is a library for Structured Output Learning. Changes:Initial Announcement on mloss.org.
