tag:joss.theoj.org,2005:/papers/edited_by/@terrytangyuanJournal of Open Source Software2021-04-30T15:30:56ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/19062021-04-30T15:30:56Z2021-05-01T00:00:58ZBetaML: The Beta Machine Learning Toolkit, a self-contained repository of Machine Learning algorithms in Juliaacceptedv0.2.22020-07-23 14:18:16 UTC602021-04-30 15:30:56 UTC620212849AntonelloLobiancoUniversité de Lorraine, Université de Strasbourg, Institut des sciences et industries du vivant et de l'environnement (AgroParisTech), Centre national de la recherche scientifique (CNRS), Institut national de recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), Bureau d'économie théorique et appliquée (BETA)0000-0002-1534-869710.21105/joss.02849https://doi.org/10.5281/zenodo.4730205Juliahttps://joss.theoj.org/papers/10.21105/joss.02849.pdfmachine learning, neural networks, deep learning, clustering, decision trees, random forest, perceptron, data sciencetag:joss.theoj.org,2005:Paper/18712020-11-07T08:20:12Z2022-01-18T11:52:28ZMLJ: A Julia package for composable machine learningacceptedv0.11.62020-07-21 21:29:04 UTC552020-11-07 08:20:12 UTC520202704AnthonyD.BlaomUniversity of Auckland, New Zealand, New Zealand eScience Infrastructure, New Zealand, Alan Turing Institute, London, United Kingdom0000-0001-6689-886XFranzKiralyAlan Turing Institute, London, United Kingdom, University College London, United Kingdom0000-0002-9254-793XThibautLienartAlan Turing Institute, London, United KingdomYiannisSimillidesImperial College London, United Kingdom0000-0002-0287-8699DiegoArenasUniversity of St Andrews, St Andrews, United Kingdom0000-0001-7829-6102SebastianJ.VollmerAlan Turing Institute, London, United Kingdom, University of Warwick, United Kingdom0000-0002-9025-075310.21105/joss.02704https://doi.org/10.5281/zenodo.4178917Jupyter Notebook, Juliahttps://joss.theoj.org/papers/10.21105/joss.02704.pdfMachine Learning, model composition, stacking, ensembling, hyper-parameter tuningtag:joss.theoj.org,2005:Paper/19372020-09-27T10:49:02Z2021-02-15T11:30:09ZFoolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAXacceptedv3.1.02020-08-10 18:30:38 UTC532020-09-27 10:49:02 UTC520202607JonasRauberTübingen AI Center, University of Tübingen, Germany, International Max Planck Research School for Intelligent Systems, Tübingen, Germany0000-0001-6795-9441RolandZimmermannTübingen AI Center, University of Tübingen, Germany, International Max Planck Research School for Intelligent Systems, Tübingen, GermanyMatthiasBethgeTübingen AI Center, University of Tübingen, Germany, Bernstein Center for Computational Neuroscience Tübingen, GermanyWielandBrendelTübingen AI Center, University of Tübingen, Germany, Bernstein Center for Computational Neuroscience Tübingen, Germany10.21105/joss.02607https://doi.org/10.5281/zenodo.4050932Python, JavaScript, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.02607.pdfpython, machine learning, adversarial attacks, neural networks, pytorch, tensorflow, jax, keras, eagerpytag:joss.theoj.org,2005:Paper/15372020-08-05T15:57:47Z2021-02-15T11:30:55ZSurprise: A Python library for recommender systemsacceptedv1.1.02020-03-02 13:50:42 UTC522020-08-05 15:57:47 UTC520202174NicolasHugColumbia University, Data Science Institute, New York City, New York, United States of America0000-0003-1360-704X10.21105/joss.02174https://doi.org/10.5281/zenodo.3959188Pythonhttps://joss.theoj.org/papers/10.21105/joss.02174.pdfRecommender systemtag:joss.theoj.org,2005:Paper/15262020-08-05T01:14:56Z2021-02-15T11:30:57Zaudiomate: A Python package for working with audio datasetsacceptedv5.2.02020-02-25 19:52:23 UTC522020-08-05 01:14:56 UTC520202135MatthiasBüchiZHAW Zurich University of Applied Sciences, Winterthur, Switzerland0000-0003-0207-5711AndreasAhlenstorfZHAW Zurich University of Applied Sciences, Winterthur, Switzerland10.21105/joss.02135https://doi.org/10.5281/zenodo.3970567Pythonhttps://joss.theoj.org/papers/10.21105/joss.02135.pdfaudio, speech, music, corpus, datasettag:joss.theoj.org,2005:Paper/15762020-06-24T15:15:36Z2021-02-15T11:30:54ZSpleeter: a fast and efficient music source separation tool with pre-trained modelsacceptedv1.4.92020-03-06 17:19:15 UTC502020-06-24 15:15:36 UTC520202154RomainHennequinDeezer Research, Paris0000-0001-8158-5562AnisKhlifDeezer Research, ParisFelixVoituretDeezer Research, ParisManuelMoussallamDeezer Research, Paris0000-0003-0886-542310.21105/joss.02154https://doi.org/10.5281/zenodo.3906389Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.02154.pdfmusical signal processing, source separation, vocal isolationtag:joss.theoj.org,2005:Paper/15102020-05-18T20:15:54Z2021-02-15T11:31:00ZPySINDy: A Python package for the sparse identification of nonlinear dynamical systems from dataacceptedv0.12.02020-02-11 01:09:30 UTC492020-05-18 20:15:54 UTC520202104BrianM.de SilvaDepartment of Applied Mathematics, University of WashingtonKathleenChampionDepartment of Applied Mathematics, University of WashingtonMarkusQuadeAmbrosys GmbHJean-ChristopheLoiseauÉcole Nationale Supérieure des Arts et MétiersJ.NathanKutzDepartment of Applied Mathematics, University of WashingtonStevenL.BruntonDepartment of Mechanical Engineering, University of Washington, Department of Applied Mathematics, University of Washington10.21105/joss.02104https://doi.org/10.5281/zenodo.3832319Pythonhttps://joss.theoj.org/papers/10.21105/joss.02104.pdfdynamical systems, sparse regression, model discovery, system identification, machine learningtag:joss.theoj.org,2005:Paper/11272020-02-06T16:49:11Z2021-02-15T11:31:54ZASCENDS: Advanced data SCiENce toolkit for Non-Data Scientistsacceptedv0.4.02019-07-24 15:01:28 UTC462020-02-06 16:49:11 UTC520201656SangkeunLeeOak Ridge National Laboratory0000-0002-1317-5112JianPengOak Ridge National Laboratory0000-0002-9763-4741AndrewWilliamsCornell UniversityDongwonShinOak Ridge National Laboratory0000-0002-5797-342310.21105/joss.01656https://doi.org/10.5281/zenodo.3635782Python, Jupyter Notebook, JavaScripthttps://joss.theoj.org/papers/10.21105/joss.01656.pdfmachine learning, artificial intelligence, neural networktag:joss.theoj.org,2005:Paper/13972020-02-05T18:14:54Z2021-02-15T11:31:16Zshapr: An R-package for explaining machine learning models with dependence-aware Shapley valuesacceptedv0.1.02019-12-10 14:11:31 UTC462020-02-05 18:14:54 UTC520192027NikolaiSellereiteNorwegian Computing Center0000-0002-4671-0337MartinJullumNorwegian Computing Center0000-0003-3908-515510.21105/joss.02027https://doi.org/10.5281/zenodo.3641831R, Python, C++https://joss.theoj.org/papers/10.21105/joss.02027.pdfexplainable AI, interpretable machine learning, shapley values, feature dependencetag:joss.theoj.org,2005:Paper/13962020-01-17T17:33:49Z2021-02-15T11:31:16Zscikit-hubness: Hubness Reduction and Approximate Neighbor Searchacceptedv0.21.12019-12-10 12:41:03 UTC452020-01-17 17:33:49 UTC520201957RomanFeldbauerDivision of Computational Systems Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Althanstraße 14, 1090 Vienna, Austria0000-0003-2216-4295ThomasRatteiDivision of Computational Systems Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Althanstraße 14, 1090 Vienna, Austria0000-0002-0592-7791ArthurFlexerAustrian Research Institute for Artificial Intelligence (OFAI), Freyung 6/6/7, 1010 Vienna, Austria0000-0002-1691-737X10.21105/joss.01957https://doi.org/10.5281/zenodo.3607202Pythonhttps://joss.theoj.org/papers/10.21105/joss.01957.pdfscikit-learn, hubness, curse of dimensionality, nearest neighbors