About: DiffSharp is a functional automatic differentiation (AD) library providing gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products as higher-order functions. It allows exact and efficient calculation of derivatives, with support for nesting. Changes:Fixed: Bug fix in forward AD implementation of Sigmoid and ReLU for D, DV, and DM (fixes #16, thank you @mrakgr) Improvement: Performance improvement by removing several more Parallel.For and Array.Parallel.map operations, working better with OpenBLAS multithreading Added: Operations involving incompatible dimensions of DV and DM will now throw exceptions for warning the user
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About: A Deep Learning API and server Changes:Initial Announcement on mloss.org.
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About: The EnsembleSVM library offers functionality to perform ensemble learning using Support Vector Machine (SVM) base models. In particular, we offer routines for binary ensemble models using SVM base classifiers. Experimental results have shown the predictive performance to be comparable with standard SVM models but with drastically reduced training time. Ensemble learning with SVM models is particularly useful for semi-supervised tasks. Changes:The library has been updated and features a variety of new functionality as well as more efficient implementations of original features. The following key improvements have been made:
The API and ABI have undergone significant changes, many of which are due to the transition to C++11.
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About: AIDE (Automata Identification Engine) is a free open source tool for automata inference algorithms developed in C# .Net. Changes:Initial Announcement on mloss.org.
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About: Regularization paTH for LASSO problem (thalasso) thalasso solves problems of the following form: minimize 1/2||X*beta-y||^2 + lambda*sum|beta_i|, where X and y are problem data and beta and lambda are variables. Changes:Initial Announcement on mloss.org.
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About: MLDemos is a user-friendly visualization interface for various machine learning algorithms for classification, regression, clustering, projection, dynamical systems, reward maximisation and reinforcement learning. Changes:New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with non-numerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bug-fixes for display, import/export of data, classification performance New Algorithms and methodologies Added Projections to pre-process data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added Grid-Search panel for batch-testing ranges of values for up to two parameters at a time Added One-vs-All multi-class classification for non-multi-class algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)
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About: TurboParser is a free multilingual dependency parser based on linear programming developed by André Martins. It is based on joint work with Noah Smith, Mário Figueiredo, Eric Xing, Pedro Aguiar. Changes:This version introduces a number of new features:
Note: The runtimes above are approximate, and based on experiments with a desktop machine with a Intel Core i7 CPU 3.4 GHz and 8GB RAM. To run this software, you need a standard C++ compiler. This software has the following external dependencies: AD3, a library for approximate MAP inference; Eigen, a template library for linear algebra; google-glog, a library for logging; gflags, a library for commandline flag processing. All these libraries are free software and are provided as tarballs in this package. This software has been tested on Linux, but it should run in other platforms with minor adaptations.
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About: 3-layer neural network for regression with sigmoid activation function and command line interface similar to LibSVM. Changes:Initial Announcement on mloss.org.
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About: A decision tree learner that is designed to be reasonably fast, but the primary goal is ease of use Changes:Initial Announcement on mloss.org.
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About: SeqAn is an open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data. Changes:
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