Projects running under macosx.
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Logo Theano 1.0.1

by jaberg - December 7, 2017, 14:14:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 38338 views, 6473 downloads, 3 subscriptions

About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano.

Changes:

Theano 1.0.1 (6th of December, 2017)

This is a maintenance release of Theano, version 1.0.1, with no new features, but some important bug fixes.

Highlights (since 1.0.0):

  • Fixed compilation and improved float16 support for topK on GPU

  • NB: topK support on GPU is experimental and may not work for large input sizes on certain GPUs

  • Fixed cuDNN reductions when axes to reduce have size 1

  • Attempted to prevent re-initialization of the GPU in a child process

  • Fixed support for temporary paths with spaces in Theano initialization

  • Spell check pass on the documentation


Logo DFLsklearn, Hyperparameters optimization in Scikit Learn 0.1

by vlatorre - November 23, 2017, 13:14:36 CET [ Project Homepage BibTeX Download ] 428 views, 95 downloads, 1 subscription

About: A method to optimize the hyperparameters for machine learning methods implemented in Scikit-learn based on Derivative Free Optimization

Changes:

Initial Announcement on mloss.org.


Logo AffectiveTweets 1.0.0

by felipebravom - November 1, 2017, 02:24:58 CET [ Project Homepage BibTeX Download ] 601 views, 194 downloads, 3 subscriptions

About: A WEKA package for analyzing emotion and sentiment of tweets.

Changes:

Initial Announcement on mloss.org.


Logo JMLR dlib ml 19.7

by davis685 - September 17, 2017, 15:10:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 206122 views, 31980 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.

Changes:

This release upgrades dlib's CNN+MMOD object detector to support creating multi-class detectors. It also includes significant speed improvements, allowing the detector to run at 98fps when executed on a NVIDIA 1080ti GPU. This release also adds a new 5 point face landmarking model that is over 10x smaller than the 68 point model, runs faster, and works with both HOG and CNN generated face detections. It is now the recommended landmarking model to use for face alignment.


Logo JMLR Jstacs 2.3

by keili - September 13, 2017, 14:25:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 32565 views, 7481 downloads, 4 subscriptions

About: A Java framework for statistical analysis and classification of biological sequences

Changes:

New classes and packages:

  • Jstacs 2.3 is the first release to be accompanied by JstacsFX, a library for building JavaFX-based graphical user interfaces based on JstacsTools
  • new interface MultiThreadedFunction
  • new class LargeSequenceReader for reading large sequence files in chunks
  • new interface QuickScanningSequenceScore
  • new class RegExpValidator for checking String inputs against a regular expression
  • new class IUPACDNAAlphabet

New features and improvements:

  • Alignments may now handle different costs for insert and delete gaps
  • ListResults may now be constructed from Collections of ResultSets
  • Several minor improvements and bugfixes in many classes
  • Improvements of documentation of several classes

Logo DynaML 1.4.1

by mandar2812 - April 20, 2017, 18:32:33 CET [ Project Homepage BibTeX Download ] 1164 views, 295 downloads, 1 subscription

About: DynaML is a Scala environment for conducting research and education in Machine Learning. DynaML comes packaged with a powerful library of classes implementing predictive models and a Scala REPL where one can not only build custom models but also play around with data work-flows.

Changes:

Initial Announcement on mloss.org.


Logo JMLR MSVMpack 1.5.1

by lauerfab - March 9, 2017, 12:29:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 31721 views, 9046 downloads, 2 subscriptions

About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini.

Changes:
  • Fix compilation error with recent gcc

Logo MIToolbox 3.0.1

by apocock - March 2, 2017, 00:38:52 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 37296 views, 6201 downloads, 3 subscriptions

About: A mutual information library for C and Mex bindings for MATLAB. Aimed at feature selection, and provides simple methods to calculate mutual information, conditional mutual information, entropy, conditional entropy, Renyi entropy/mutual information, and weighted variants of Shannon entropies/mutual informations. Works with discrete distributions, and expects column vectors of features.

Changes:

Fixed a Windows compilation bug. MIToolbox v3 should now compile using Visual Studio.


Logo LogRegCrowds, Logistic Regression from Crowds 1.0

by fmpr - January 16, 2017, 18:10:57 CET [ Project Homepage BibTeX Download ] 4555 views, 1135 downloads, 3 subscriptions

About: LogReg-Crowds is a collection of Julia implementations of various approaches for learning a logistic regression model multiple annotators and crowds, namely the works of Raykar et al. (2010), Rodrigues et al. (2013) and Dawid and Skene (1979).

Changes:

Initial Announcement on mloss.org. Added GitHub page.


Logo FEAST 2.0.0

by apocock - January 8, 2017, 00:49:19 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 50009 views, 8642 downloads, 4 subscriptions

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About: FEAST provides implementations of common mutual information based filter feature selection algorithms (mim, mifs, mrmr, cmim, icap, jmi, disr, fcbf, etc), and an implementation of RELIEF. Written for C/C++ & Matlab.

Changes:

Major refactoring of FEAST to improve speed and portability.

  • FEAST now clones the input data if it's floating point and discretises it to unsigned ints once in a single pass. This improves the speed by about 30%.
  • FEAST now has unsigned int entry points which avoid this discretisation and are much faster if the data is already categorical.
  • Added weighted feature selection algorithms to FEAST which can be used for cost-sensitive feature selection.
  • Added a Java API using JNI.
  • FEAST now returns the internal score for each feature according to the criterion. Available in all three APIs.
  • Rearranged the repository to make it easier to work with. Header files are now in `include`, source in `src`, the MATLAB API is in `matlab/` and the Java API is in `java/`.
  • FEAST now compiles cleanly using `-std=c89 -Wall -Werror`.

Logo WEKA 3.9.1

by mhall - December 19, 2016, 04:44:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 77981 views, 17334 downloads, 5 subscriptions

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About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

Changes:

In core weka:

  • JAMA-based linear algebra routines replaced with MTJ. Faster operation with the option to use native libraries for even more speed
  • General efficiency improvements in core, filters and some classifiers
  • GaussianProcesses now handles instance weights
  • New Knowledge Flow implementation. Engine completely rewritten from scratch with a simplified API
  • New Workbench GUI
  • GUI package manager now has a search facility
  • FixedDictionaryStringToWordVector filter allows the use of an external dictionary for vectorization. DictionarySaver converter can be used to create a dictionary file

In packages:

  • Packages that were using JAMA are now using MTJ
  • New netlibNativeOSX, netlibNativeWindows and netlibNativeLinux packages providing native reference implementations (and system-optimized implementation in the case of OSX) of BLAS, LAPACK and ARPACK linear algebra
  • New elasticNet package, courtesy of Nikhil Kinshore
  • New niftiLoader package for loading a directory with MIR data in NIfTI format into Weka
  • New percentageErrorMetrics package - provides plugin evaluation metrics for root mean square percentage error and mean absolute percentage error
  • New iterativeAbsoluteErrorRegression package - provides a meta learner that fits a regression model to minimize absolute error
  • New largeScaleKernelLearning package - contains filters for large-scale kernel-based learning
  • discriminantAnalysis package now contains an implementation for LDA and QDA
  • New Knowledge Flow component implementations in various packages
  • newKnowledgeFlowStepExamples package - contains code examples for new Knowledge Flow API discussion in the Weka Manual
  • RPlugin updated to latest version of MLR
  • scatterPlot3D and associationRulesVisualizer packages updated with latest Java 3D libraries
  • Support for pluggable activation functions in the multiLayerPerceptrons package

Logo JMLR scikitlearn 0.18.1

by fabianp - November 28, 2016, 17:45:27 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 34755 views, 12879 downloads, 5 subscriptions

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About: The scikit-learn project is a machine learning library in Python.

Changes:

Update for 0.18 .1


Logo hca 0.63

by wbuntine - April 26, 2016, 15:35:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 35384 views, 4410 downloads, 4 subscriptions

About: Multi-core non-parametric and bursty topic models (HDP-LDA, DCMLDA, and other variants of LDA) implemented in C using efficient Gibbs sampling, with hyperparameter sampling and other flexible controls.

Changes:

Corrected the new normalised Gamma model for topics so it works with multicore. Improvements to documentation. Added an asymptotic version of the generalised Stirling numbers so it longer fails when they run out of bounds on bigger data.


Logo YCML 0.2.2

by yconst - August 24, 2015, 20:28:45 CET [ Project Homepage BibTeX Download ] 2630 views, 587 downloads, 3 subscriptions

About: A Machine Learning framework for Objective-C and Swift (OS X / iOS)

Changes:

Initial Announcement on mloss.org.


Logo JMLR libDAI 0.3.2

by jorism - July 17, 2015, 15:59:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 61851 views, 11808 downloads, 4 subscriptions

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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.


Logo FsAlg 0.5.4

by gbaydin - April 25, 2015, 02:11:03 CET [ Project Homepage BibTeX Download ] 2569 views, 692 downloads, 1 subscription

About: FsAlg is a linear algebra library that supports generic types.

Changes:

Initial Announcement on mloss.org.


Logo CN24 Convolutional Neural Networks for Semantic Segmentation 1.0

by erik - February 23, 2015, 09:02:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3853 views, 789 downloads, 1 subscription

About: CN24 is a complete semantic segmentation framework using fully convolutional networks.

Changes:

Initial Announcement on mloss.org.


Logo JMLR SHOGUN 4.0.0

by sonne - February 5, 2015, 09:09:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 137136 views, 19717 downloads, 6 subscriptions

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About: The SHOGUN machine learning toolbox's focus is on large scale learning methods with focus on Support Vector Machines (SVM), providing interfaces to python, octave, matlab, r and the command line.

Changes:

This release features the work of our 8 GSoC 2014 students [student; mentors]:

  • OpenCV Integration and Computer Vision Applications [Abhijeet Kislay; Kevin Hughes]
  • Large-Scale Multi-Label Classification [Abinash Panda; Thoralf Klein]
  • Large-scale structured prediction with approximate inference [Jiaolong Xu; Shell Hu]
  • Essential Deep Learning Modules [Khaled Nasr; Sergey Lisitsyn, Theofanis Karaletsos]
  • Fundamental Machine Learning: decision trees, kernel density estimation [Parijat Mazumdar ; Fernando Iglesias]
  • Shogun Missionary & Shogun in Education [Saurabh Mahindre; Heiko Strathmann]
  • Testing and Measuring Variable Interactions With Kernels [Soumyajit De; Dino Sejdinovic, Heiko Strathmann]
  • Variational Learning for Gaussian Processes [Wu Lin; Heiko Strathmann, Emtiyaz Khan]

It also contains several cleanups and bugfixes:

Features

  • New Shogun project description [Heiko Strathmann]
  • ID3 algorithm for decision tree learning [Parijat Mazumdar]
  • New modes for PCA matrix factorizations: SVD & EVD, in-place or reallocating [Parijat Mazumdar]
  • Add Neural Networks with linear, logistic and softmax neurons [Khaled Nasr]
  • Add kernel multiclass strategy examples in multiclass notebook [Saurabh Mahindre]
  • Add decision trees notebook containing examples for ID3 algorithm [Parijat Mazumdar]
  • Add sudoku recognizer ipython notebook [Alejandro Hernandez]
  • Add in-place subsets on features, labels, and custom kernels [Heiko Strathmann]
  • Add Principal Component Analysis notebook [Abhijeet Kislay]
  • Add Multiple Kernel Learning notebook [Saurabh Mahindre]
  • Add Multi-Label classes to enable Multi-Label classification [Thoralf Klein]
  • Add rectified linear neurons, dropout and max-norm regularization to neural networks [Khaled Nasr]
  • Add C4.5 algorithm for multiclass classification using decision trees [Parijat Mazumdar]
  • Add support for arbitrary acyclic graph-structured neural networks [Khaled Nasr]
  • Add CART algorithm for classification and regression using decision trees [Parijat Mazumdar]
  • Add CHAID algorithm for multiclass classification and regression using decision trees [Parijat Mazumdar]
  • Add Convolutional Neural Networks [Khaled Nasr]
  • Add Random Forests algorithm for ensemble learning using CART [Parijat Mazumdar]
  • Add Restricted Botlzmann Machines [Khaled Nasr]
  • Add Stochastic Gradient Boosting algorithm for ensemble learning [Parijat Mazumdar]
  • Add Deep contractive and denoising autoencoders [Khaled Nasr]
  • Add Deep belief networks [Khaled Nasr]

Bugfixes

  • Fix reference counting bugs in CList when reference counting is on [Heiko Strathmann, Thoralf Klein, lambday]
  • Fix memory problem in PCA::apply_to_feature_matrix [Parijat Mazumdar]
  • Fix crash in LeastAngleRegression for the case D greater than N [Parijat Mazumdar]
  • Fix memory violations in bundle method solvers [Thoralf Klein]
  • Fix fail in library_mldatahdf5.cpp example when http://mldata.org is not working properly [Parijat Mazumdar]
  • Fix memory leaks in Vowpal Wabbit, LibSVMFile and KernelPCA [Thoralf Klein]
  • Fix memory and control flow issues discovered by Coverity [Thoralf Klein]
  • Fix R modular interface SWIG typemap (Requires SWIG >= 2.0.5) [Matt Huska]

Cleanup and API Changes

  • PCA now depends on Eigen3 instead of LAPACK [Parijat Mazumdar]
  • Removing redundant and fixing implicit imports [Thoralf Klein]
  • Hide many methods from SWIG, reducing compile memory by 500MiB [Heiko Strathmann, Fernando Iglesias, Thoralf Klein]

About: This library implements the Optimum-Path Forest classifier for unsupervised and supervised learning.

Changes:

Initial Announcement on mloss.org.


Logo JMLR Waffles 2014-07-05

by mgashler - July 20, 2014, 04:53:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 48764 views, 11812 downloads, 2 subscriptions

About: Script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a public domain C++ class library.)

Changes:

Added support for CUDA GPU-parallelized neural network layers, and several other new features. Full list of changes at http://waffles.sourceforge.net/docs/changelog.html


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