Machine Learning Open Source Software

To support the open source software movement, JMLR MLOSS publishes contributions related to implementations of non-trivial machine learning algorithms, toolboxes or even languages for scientific computing. Submission instructions are available here.

A Library for Locally Weighted Projection Regression: Stefan Klanke, Sethu Vijayakumar, Stefan Schaal; 9(Apr):623--626, 2008.
[abs][pdf] [code][mloss.org]

Shark: Christian Igel, Verena Heidrich-Meisner, Tobias Glasmachers; 9(Jun):993--996, 2008.
[abs][pdf] [code][mloss.org]

LIBLINEAR: A Library for Large Linear Classification: Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin; 9(Aug):1871--1874, 2008.
[abs][pdf] [code][mloss.org]

JNCC2: The Java Implementation Of Naive Credal Classifier 2: Giorgio Corani, Marco Zaffalon; 9(Dec):2695--2698, 2008.
[abs][pdf] [code][mloss.org]

Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data: Abhik Shah, Peter Woolf; 10(Feb):159--162, 2009.
[abs][pdf] [code][mloss.org]

Nieme: Large-Scale Energy-Based Models: Francis Maes; 10(Mar):743--746, 2009.
[abs][pdf] [code][mloss.org]

Java-ML: A Machine Learning Library: Thomas Abeel, Yves Van de Peer, Yvan Saeys; 10(Apr):931--934, 2009.
[abs][pdf] [code][mloss.org]

Model Monitor (M²): Evaluating, Comparing, and Monitoring Models: Troy Raeder, Nitesh V. Chawla; 10(Jul):1387--1390, 2009.
[abs][pdf] [code][mloss.org]

Dlib-ml: A Machine Learning Toolkit: Davis E. King; 10(Jul):1755--1758, 2009.
[abs][pdf] [code][mloss.org]

RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments: Brian Tanner, Adam White; 10(Sep):2133--2136, 2009.
[abs][pdf] [code][mloss.org]

DL-Learner: Learning Concepts in Description Logics: Jens Lehmann; 10(Nov):2639−2642, 2009.
[abs][pdf] [code][mloss.org]

Error-Correcting Output Codes Library: Sergio Escalera, Oriol Pujol, Petia Radeva; 11(Feb):661−664, 2010.
[abs][pdf] [code][mloss.org]

PyBrain: Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber; 11(Feb):743−746, 2010.
[abs][pdf] [code][mloss.org]

Continuous Time Bayesian Network Reasoning and Learning Engine: Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu; 11(Mar):1137−1140, 2010.
[abs][pdf] [code][mloss.org]

SFO: A Toolbox for Submodular Function Optimization: Andreas Krause; 11(Mar):1141−1144, 2010.
[abs][pdf] [code][mloss.org]

MOA: Massive Online Analysis: Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer; 11(May):1601−1604, 2010.
[abs][pdf] [code][mloss.org]

FastInf: An Efficient Approximate Inference Library: Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan; 11(May):1733−1736, 2010.
[abs][pdf] [code][mloss.org]

The SHOGUN Machine Learning Toolbox: Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio de Bona, Alexander Binder, Christian Gehl, Vojtěch Franc; 11(Jun):1799−1802, 2010.
[abs][pdf] [code][mloss.org]

A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design: Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom Dhaene, Karel Crombecq; 11(Jul):2051−2055, 2010.
[abs][pdf] [code][mloss.org]

Model-based Boosting 2.0: Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner; 11(Aug):2109−2113, 2010.
[abs][pdf] [code][mloss.org]

libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models: Joris M. Mooij; 11(Aug):2169−2173, 2010.
[abs][pdf] [code][mloss.org]

Gaussian Processes for Machine Learning (GPML) Toolbox: Carl Edward Rasmussen, Hannes Nickisch; 11(Nov):3011−3015, 2010.
[abs][pdf] [code][mloss.org]

CARP: Software for Fishing Out Good Clustering Algorithms: Volodymyr Melnykov, Ranjan Maitra; 12(Jan):69−73, 2011.
[abs][pdf] [code][mloss.org]

The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets: Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, Christian Buchta; 12(Jun):2021−2025, 2011.
[abs][pdf] [code][mloss.org]

MSVMpack: A Multi-Class Support Vector Machine Package: Fabien Lauer, Yann Guermeur; 12(Jul):2293−2296, 2011.
[abs][pdf] [code][mloss.org]

Waffles: A Machine Learning Toolkit: Michael Gashler; 12(Jul):2383−2387, 2011.
[abs][pdf] [code][mloss.org]

MULAN: A Java Library for Multi-Label Learning: Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Jozef Vilcek, Ioannis Vlahavas; 12(Jul):2411−2414, 2011.
[abs][pdf] [code][mloss.org]

LPmade: Link Prediction Made Easy: Ryan N. Lichtenwalter, Nitesh V. Chawla; 12(Aug):2489−2492, 2011.
[abs][pdf] [code][mloss.org]

Scikit-learn: Machine Learning in Python: Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay; 12(Oct):2825−2830, 2011.
[abs][pdf] [code][mloss.org]

The Stationary Subspace Analysis Toolbox: Jan Saputra Müller, Paul von Bünau, Frank C. Meinecke, Franz J. Király, Klaus-Robert Müller; 12(Oct):3065−3069, 2011.
[abs][pdf] [code][mloss.org]

MULTIBOOST: A Multi-purpose Boosting Package: Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl; 13(Mar):549−553, 2012.
[abs][pdf] [code][mloss.org]

ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel: Stephen R. Piccolo, Lewis J. Frey; 13(Mar):555−559, 2012.
[abs][pdf] [code][sourceforge.net]

GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression: Chiwoo Park, Jianhua Z. Huang, Yu Ding; 13(Mar):775−779, 2012.
[abs][pdf] [code][mloss.org]

NIMFA : A Python Library for Nonnegative Matrix Factorization: Marinka Žitnik, Blaž Zupan; 13(Mar):849−853, 2012.
[abs][pdf] [code][mloss.org]

The huge Package for High-dimensional Undirected Graph Estimation in R: Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman; 13(Apr):1059−1062, 2012.
[abs][pdf] [code][cran.r-project.org]

glm-ie: Generalised Linear Models Inference & Estimation Toolbox: Hannes Nickisch; 13(May):1699−1703, 2012.
[abs][pdf] [code][mloss.org]

Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences: Jan Grau, Jens Keilwagen, André Gohr, Berit Haldemann, Stefan Posch, Ivo Grosse; 13(Jun):1967−1971, 2012.
[abs][pdf] [code][mloss.org]

Pattern for Python: Tom De Smedt, Walter Daelemans; 13(Jun):2063−2067, 2012.
[abs][pdf] [code][mloss.org]

DEAP: Evolutionary Algorithms Made Easy: Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, Christian Gagné; 13(Jul):2171−2175, 2012.
[abs][pdf] [code][deap.gel.ulaval.ca]

A Topic Modeling Toolbox Using Belief Propagation: Jia Zeng; 13(Jul):2233−2236, 2012.
[abs][pdf] [code][mloss.org]

PREA: Personalized Recommendation Algorithms Toolkit: Joonseok Lee, Mingxuan Sun, Guy Lebanon; 13(Sep):2699−2703, 2012.
[abs][pdf] [code][mloss.org]

Oger: Modular Learning Architectures For Large-Scale Sequential Processing: David Verstraeten, Benjamin Schrauwen, Sander Dieleman, Philemon Brakel, Pieter Buteneers, Dejan Pecevski; 13(Oct):2995−2998, 2012.
[abs][pdf] [code][mloss.org]

Sally: A Tool for Embedding Strings in Vector Spaces: Konrad Rieck, Christian Wressnegger, Alexander Bikadorov; 13(Nov):3247−3251, 2012.
[abs][pdf] [code][mloss.org]

DARWIN: A Framework for Machine Learning and Computer Vision Research and Development: Stephen Gould; 13(Dec):3533−3537, 2012.
[abs][pdf] [code][mloss.org]

SVDFeature: A Toolkit for Feature-based Collaborative Filtering: Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu; 13(Dec):3619−3622, 2012.
[abs][pdf] [code][mloss.org]

A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics: Hervé Frezza-Buet, Matthieu Geist; 14(Feb):625−628, 2013.
[abs][pdf] [code][malis.metz.supelec.fr]

MLPACK: A Scalable C++ Machine Learning Library: Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram, Nishant A. Mehta, Alexander G. Gray; 14(Mar):801−805, 2013.
[abs][pdf] [code][mloss.org]

GPstuff: Bayesian Modeling with Gaussian Processes: Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari; 14(Apr):1175−1179, 2013.
[abs][pdf] [code][mloss.org]

JKernelMachines: A Simple Framework for Kernel Machines: David Picard, Nicolas Thome, Matthieu Cord; 14(May):1417−1421, 2013.
[abs][pdf][bib] [code][mloss.org]

Orange: Data Mining Toolbox in Python: Janez Demšar, Tomaž Curk, Aleš Erjavec, Črt Gorup, Tomaž Hočevar, Mitar Milutinovič, Martin Možina, Matija Polajnar, Marko Toplak, Anže Starič, Miha Štajdohar, Lan Umek, Lan Žagar, Jure Žbontar, Marinka Žitnik, Blaž Zupan; 14(Aug):2349−2353, 2013.
[abs][pdf][bib] [code][mloss.org]

Tapkee: An Efficient Dimension Reduction Library: Sergey Lisitsyn, Christian Widmer, Fernando J. Iglesias Garcia; 14(Aug):2355−2359, 2013.
[abs][pdf][bib] [code][mloss.org]

The CAM Software for Nonnegative Blind Source Separation in R-Java: Niya Wang, Fan Meng, Li Chen, Subha Madhavan, Robert Clarke, Eric P. Hoffman, Jianhua Xuan, Yue Wang; 14(Sep):2899−2903, 2013.
[abs][pdf][bib] [code][mloss.org]

QuantMiner for Mining Quantitative Association Rules: Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, Daniel Cassard; 14(Oct):3153−3157, 2013.
[abs][pdf][bib] [code][github.com]

Divvy: Fast and Intuitive Exploratory Data Analysis: Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten; 14(Oct):3159−3163, 2013.
[abs][pdf][bib] [code][mloss.org]

GURLS: A Least Squares Library for Supervised Learning: Andrea Tacchetti, Pavan K. Mallapragada, Matteo Santoro, Lorenzo Rosasco; 14(Oct):3201−3205, 2013.
[abs][pdf][bib] [code][github.com]

BudgetedSVM: A Toolbox for Scalable SVM Approximations: Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang; 14(Dec):3813−3817, 2013.
[abs][pdf][bib] [code][temple.edu]

EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines: Marc Claesen, Frank De Smet, Johan A.K. Suykens, Bart De Moor; 15(Jan):141−145, 2014.
[abs][pdf][bib] [code][mloss.org]

Information Theoretical Estimators Toolbox: Zoltán Szabó; 15(Jan):283−287, 2014.
[abs][pdf][bib] [code][mloss.org]

The FASTCLIME Package for Linear Programming and Large-Scale Precision Matrix Estimation in R: Haotian Pang, Han Liu, Robert Vanderbei; 15(Feb):489−493, 2014.
[abs][pdf][bib] [code][mloss.org]

LIBOL: A Library for Online Learning Algorithms: Steven C.H. Hoi, Jialei Wang, Peilin Zhao; 15(Feb):495−499, 2014.
[abs][pdf][bib] [code][mloss.org]

Conditional Random Field with High-order Dependencies for Sequence Labeling and Segmentation: Nguyen Viet Cuong, Nan Ye, Wee Sun Lee, Hai Leong Chieu; 15(Mar):981−1009, 2014.
[abs][pdf][bib] [code][github.com]

Manopt, a Matlab Toolbox for Optimization on Manifolds: Nicolas Boumal, Bamdev Mishra, P.-A. Absil, Rodolphe Sepulchre; 15(Apr):1455−1459, 2014.
[abs][pdf][bib] [code][manopt.org]

pystruct - Learning Structured Prediction in Python: Andreas C. Müller, Sven Behnke; 15(Jun):2055−2060, 2014.
[abs][pdf][bib] [code][github.io]

ooDACE Toolbox: A Flexible Object-Oriented Kriging Implementation: Ivo Couckuyt, Tom Dhaene, Piet Demeester; 15(Oct):3183−3186, 2014.
[abs][pdf][bib] [code][sumo.intec.ugent.be]

The Gesture Recognition Toolkit: Nicholas Gillian, Joseph A. Paradiso; 15(Oct):3483−3487, 2014.
[abs][pdf][bib] [code][github.com]

SPMF: A Java Open-Source Pattern Mining Library: Philippe Fournier Viger, Antonio Gomariz, Ted Gueniche, Azadeh Soltani, Cheng-Wei Wu, Vincent S. Tseng; 15(Nov):3389−3393, 2014.
[abs][pdf][bib] [code][www.philippe-fournier-viger.com]

BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits: Ruben Martinez-Cantin; 15(Nov):3735−3739, 2014.
[abs][pdf][bib] [code][bitbucket.org]

SAMOA: Scalable Advanced Massive Online Analysis: Gianmarco De Francisci Morales, Albert Bifet; 16(Jan):149−153, 2015.
[abs][pdf][bib] [code][samoa-project.net]

The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R: Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu; 16(Mar):553−557, 2015.
[abs][pdf][bib] [code][cran.r-project.org]

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit: Felix Weninger; 16(Mar):547−551, 2015.
[abs][pdf][bib] [code][sourceforge.net]

A Classification Module for Genetic Programming Algorithms in JCLEC: Alberto Cano, José María Luna, Amelia Zafra, Sebastián Ventura; 16(Mar):491−494, 2015.
[abs][pdf][bib] [code][jclec.sourceforge.net]

Encog: Library of Interchangeable Machine Learning Models for Java and C#: Jeff Heaton; 16(Jun):1243−1247, 2015.
[abs][pdf][bib] [code][www.encog.org]

RLPy: A Value-Function-Based Reinforcement Learning Framework for Education and Research: Alborz Geramifard, Christoph Dann, Robert H. Klein, William Dabney, Jonathan P. How; 16(Aug):1573−1578, 2015.
[abs][pdf][bib] [code][github]

The Libra Toolkit for Probabilistic Models: Daniel Lowd, Amirmohammad Rooshenas; 16(Dec):2459−2463, 2015.
[abs][pdf][bib] [code][uoregon.edu]

pyGPs -- A Python Library for Gaussian Process Regression and Classification: Marion Neumann, Shan Huang, Daniel E. Marthaler, Kristian Kersting; 16(Dec):2611−2616, 2015.
[abs][pdf][bib] [code][github]

CEKA: A Tool for Mining the Wisdom of Crowds: Jing Zhang, Victor S. Sheng, Bryce A. Nicholson, Xindong Wu; 16(Dec):2853−2858, 2015.
[abs][pdf][bib] [code][sourceforge]

SnFFT: A Julia Toolkit for Fourier Analysis of Functions over Permutations: Gregory Plumb, Deepti Pachauri, Risi Kondor, Vikas Singh; 16(Dec):3469−3473, 2015.
[abs][pdf][bib] [code][github]

partykit: A Modular Toolkit for Recursive Partytioning in R: Torsten Hothorn, Achim Zeileis; 16(Dec):3905−3909, 2015.
[abs][pdf][bib] [code][cran]

Harry: A Tool for Measuring String Similarity: Konrad Rieck, Christian Wressnegger; 17(9):1−5, 2016.
[abs][pdf][bib] [code][mlsec.org]

MEKA: A Multi-label/Multi-target Extension to WEKA: Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes; 17(21):1−5, 2016.
[abs][pdf][bib] [code][sourceforge]

MLlib: Machine Learning in Apache Spark: Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar; 17(34):1−7, 2016.
[abs][pdf][bib] [code][apache.org]

OLPS: A Toolbox for On-Line Portfolio Selection: Bin Li, Doyen Sahoo, Steven C.H. Hoi; 17(35):1−5, 2016.
[abs][pdf][bib] [code][github]

BayesPy: Variational Bayesian Inference in Python: Jaakko Luttinen; 17(41):1−6, 2016.
[abs][pdf][bib] [appendix] [code][bayespy.org]

StructED: Risk Minimization in Structured Prediction: Yossi Adi, Joseph Keshet; 17(64):1−5, 2016.
[abs][pdf][bib] [code][github]

CVXPY: A Python-Embedded Modeling Language for Convex Optimization: Steven Diamond, Stephen Boyd; 17(83):1−5, 2016.
[abs][pdf][bib] [code][cvxpy.org]

LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems: Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin; 17(86):1−5, 2016.
[abs][pdf][bib] [code][ntu.edu.tw]

JCLAL: A Java Framework for Active Learning: Oscar Reyes, Eduardo Pérez, María del Carmen Rodríguez-Hernández, Habib M. Fardoun, Sebastián Ventura; 17(95):1−5, 2016.
[abs][pdf][bib] [code][sourceforge]

The LRP Toolbox for Artificial Neural Networks: Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek; 17(114):1−5, 2016.
[abs][pdf][bib] [code][heatmapping.org]

Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation: James Townsend, Niklas Koep, Sebastian Weichwald; 17(137):1−5, 2016.
[abs][pdf][bib] [code][github]

Megaman: Scalable Manifold Learning in Python: James McQueen, Marina Meilă, Jacob VanderPlas, Zhongyue Zhang; 17(148):1−5, 2016.
[abs][pdf][bib] [code][github]

mlr: Machine Learning in R: Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, Zachary M. Jones; 17(170):1−5, 2016.
[abs][pdf][bib] [code][github]

bandicoot: a Python Toolbox for Mobile Phone Metadata: Yves-Alexandre de Montjoye, Luc Rocher, Alex Sandy Pentland; 17(175):1−5, 2016.
[abs][pdf][bib] [code][mit.edu]

fastFM: A Library for Factorization Machines: Immanuel Bayer; 17(184):1−5, 2016.
[abs][pdf][bib] [code][github]

RLScore: Regularized Least-Squares Learners: Tapio Pahikkala, Antti Airola; 17(221):1−5, 2016.
[abs][pdf][bib] [code][cs.utu.fi]

SnapVX: A Network-Based Convex Optimization Solver: David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosič, Stephen Boyd, Jure Leskovec; 18(4):1−5, 2017.
[abs][pdf][bib] [code][stanford.edu]

Refinery: An Open Source Topic Modeling Web Platform: Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth; 18(12):1−5, 2017.
[abs][pdf][bib] [code][github]

Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning: Guillaume Lemaître, Fernando Nogueira, Christos K. Aridas; 18(17):1−5, 2017.
[abs][pdf][bib] [code][contrib.scikit-learn.org]

JSAT: Java Statistical Analysis Tool, a Library for Machine Learning: Edward Raff; 18(23):1−5, 2017.
[abs][pdf][bib] [code][github]

Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA: Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown; 18(25):1−5, 2017.
[abs][pdf][bib] [code][ubc.ca]

POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty: Maxim Egorov, Zachary N. Sunberg, Edward Balaban, Tim A. Wheeler, Jayesh K. Gupta, Mykel J. Kochenderfer; 18(26):1−5, 2017.
[abs][pdf][bib] [code][github]

GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis: Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski; 18(39):1−5, 2017.
[abs][pdf][bib] [code][r-project.org]

GPflow: A Gaussian Process Library using TensorFlow: Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman; 18(40):1−6, 2017.
[abs][pdf][bib] [code][github]

The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems: Frans A. Oliehoek, Matthijs T. J. Spaan, Bas Terwijn, Philipp Robbel, João V. Messias; 18(89):1−5, 2017.
[abs][pdf][bib] [code][github]

openXBOW -- Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit: Maximilian Schmitt, Björn Schuller; 18(96):1−5, 2017.
[abs][pdf][bib] [code][github]

HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data: Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning; 18(152):1−6, 2018.
[abs][pdf][bib] [code][readthedocs.io]

pomegranate: Fast and Flexible Probabilistic Modeling in Python: Jacob Schreiber; 18(164):1−6, 2018.
[abs][pdf][bib] [code][readthedocs.io]

auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks: Michael Freitag, Shahin Amiriparian, Sergey Pugachevskiy, Nicholas Cummins, Björn Schuller; 18(173):1−5, 2018.
[abs][pdf][bib] [code]

Pycobra: A Python Toolbox for Ensemble Learning and Visualisation: Benjamin Guedj, Bhargav Srinivasa Desikan; 18(190):1−5, 2018.
[abs][pdf][bib] [github.com][inria.fr]

KELP: a Kernel-based Learning Platform: Simone Filice, Giuseppe Castellucci, Giovanni Da San Martino, Alessandro Moschitti, Danilo Croce, Roberto Basili; 18(191):1−5, 2018.
[abs][pdf][bib] [github.com][kelp-ml.org]

tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models: Emmanuel Bacry, Martin Bompaire, Philip Deegan, Stéphane Gaïffas, Søren V. Poulsen; 18(214):1−5, 2018.
[abs][pdf][bib] [github.com][x-datainitiative.github.io]

SGDLibrary: A MATLAB library for stochastic optimization algorithms: Hiroyuki Kasai; 18(215):1−5, 2018.
[abs][pdf][bib] [github.com]

ELFI: Engine for Likelihood-Free Inference: Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski; (16):1−7, 2018.
[abs][pdf][bib] [readthedocs.io] [github.com]

ThunderSVM: A Fast SVM Library on GPUs and CPUs: Zeyi Wen, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen; (21):1−5, 2018.
[abs][pdf][bib] [readthedocs.io] [github.com]

OpenEnsembles: A Python Resource for Ensemble Clustering: Tom Ronan, Shawn Anastasio, Zhijie Qi, Pedro Henrique S. Vieira Tavares, Roman Sloutsky, Kristen M. Naegle; (26):1−6, 2018.
[abs][pdf][bib] [github.io] [github.com]

Editorial Board

Open Source Software