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Logo MLweb 0.1.4

by lauerfab - June 28, 2016, 16:00:52 CET [ Project Homepage BibTeX Download ] 3933 views, 964 downloads, 3 subscriptions

About: MLweb is an open source project that aims at bringing machine learning capabilities into web pages and web applications, while maintaining all computations on the client side. It includes (i) a javascript library to enable scientific computing within web pages, (ii) a javascript library implementing machine learning algorithms for classification, regression, clustering and dimensionality reduction, (iii) a web application providing a matlab-like development environment.

  • Add Logistic Regression
  • Add support for sparse input in fast training of linear SVM
  • Better support for sparse vectors/matrices
  • Minor bug fixes

About: Nowadays this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use a stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many abilities such as feature extraction and classification that are used in many applications including image processing, speech processing, text categorization, etc. This paper introduces a new object oriented toolbox with the most important abilities needed for the implementation of DBNs. According to the results of the experiments conducted on the MNIST (image), ISOLET (speech), and the 20 Newsgroups (text) datasets, it was shown that the toolbox can learn automatically a good representation of the input from unlabeled data with better discrimination between different classes. Also on all the aforementioned datasets, the obtained classification errors are comparable to those of the state of the art classifiers. In addition, the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our new FEPCD method), different sparsity methods (quadratic, rate distortion and our new normal method), different RBM types (generative and discriminative), GPU based, etc. The toolbox is a user-friendly open source software in MATLAB and Octave and is freely available on the website.


New in toolbox

  • Using GPU in Backpropagation
  • Revision of some demo scripts
  • Function approximation with multiple outputs
  • Feature extraction with GRBM in first layer


Logo JMLR dlib ml 19.0

by davis685 - June 25, 2016, 23:04:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 142051 views, 23185 downloads, 4 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 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.

Logo revrand 0.4.1

by dsteinberg - June 24, 2016, 05:58:05 CET [ Project Homepage BibTeX Download ] 2852 views, 557 downloads, 3 subscriptions

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

  • Allow for non-learnable likelihood arguments (per datum) in the glm
  • Hotfix for glm prediction sampling functions

Logo JMLR MLPACK 2.0.2

by rcurtin - June 20, 2016, 22:23:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 59774 views, 10966 downloads, 6 subscriptions

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About: A scalable, fast C++ machine learning library, with emphasis on usability.

  • Added the function LSHSearch::Projections(), which returns an arma::cube with each projection table in a slice (#663). Instead of Projection(i), you should now use Projections().slice(i).
  • A new constructor has been added to LSHSearch that creates objects using projection tables provided in an arma::cube (#663).
  • LSHSearch projection tables refactored for speed (#675).
  • Handle zero-variance dimensions in DET (#515).
  • Add MiniBatchSGD optimizer (src/mlpack/core/optimizers/minibatch_sgd/) and allow its use in mlpack_logistic_regression and mlpack_nca programs.
  • Add better backtrace support from Grzegorz Krajewski for Log::Fatal messages when compiled with debugging and profiling symbols. This requires libbfd and libdl to be present during compilation.
  • CosineTree test fix from Mikhail Lozhnikov (#358).
  • Fixed HMM initial state estimation (#600).
  • Changed versioning macros _MLPACKVERSION_MAJOR, _MLPACKVERSION_MINOR, and _MLPACKVERSION_PATCH to MLPACK_VERSION_MAJOR, MLPACK_VERSION_MINOR, and MLPACK_VERSION_PATCH. The old names will remain in place until mlpack 3.0.0.
  • Renamed mlpack_allknn, mlpack_allkfn, and mlpack_allkrann to mlpack_knn, mlpack_kfn, and mlpack_krann. The mlpack_allknn, mlpack_allkfn, and mlpack_allkrann programs will remain as copies until mlpack 3.0.0.
  • Add --random_initialization option to mlpack_hmm_train, for use when no labels are provided.
  • Add --kill_empty_clusters option to mlpack_kmeans and KillEmptyClusters policy for the KMeans class (#595, #596).

Logo SparklingGraph 0.0.6

by riomus - June 17, 2016, 14:49:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2039 views, 361 downloads, 2 subscriptions

About: Large scale, distributed graph processing made easy.


Bug fixes, Graph generators

Logo Salad 0.6.1

by chwress - June 17, 2016, 11:26:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10908 views, 2053 downloads, 3 subscriptions

About: A Content Anomaly Detector based on n-Grams


A teeny tiny fix to correctly handle input strings shorter than a registers width

Logo scikit multilearn 0.0.3

by niedakh - June 15, 2016, 19:28:32 CET [ Project Homepage BibTeX Download ] 417 views, 89 downloads, 2 subscriptions

About: A native Python, scikit-compatible, implementation of a variety of multi-label classification algorithms.


Initial Announcement on

Logo ADENINE 0.1.3

by samuelefiorini - June 13, 2016, 11:10:36 CET [ Project Homepage BibTeX Download ] 397 views, 59 downloads, 2 subscriptions

About: ADENINE (A Data ExploratioN pIpeliNE) is a machine learning framework for data exploration that encompasses state-of-the-art techniques for missing values imputing, data preprocessing, dimensionality reduction and clustering tasks.


Initial Announcement on

Logo JMLR Information Theoretical Estimators 0.63

by szzoli - June 9, 2016, 23:42:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 102243 views, 19679 downloads, 3 subscriptions

About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems.

  • Conditional Shannon entropy estimation: added.

  • Conditional Shannon mutual information estimation: included.

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