tag:joss.theoj.org,2005:/papers/tagged/machine%20learningJournal of Open Source Software2024-03-27T23:48:03ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/51402024-03-27T23:48:03Z2024-03-28T00:11:53ZTDApplied: An R package for machine learning and inference with persistence diagramsacceptedv3.0.22024-01-25 19:46:31 UTC952024-03-27 23:48:03 UTC920246321ShaelBrownDepartment of Quantitative Life Sciences, McGill University, Montreal, Canada0000-0001-8868-2867RezaFarivar-MohseniMcGill Vision Research, Department of Opthamology, McGill University, Montreal, Canada0000-0002-3123-262710.21105/joss.06321https://doi.org/10.5281/zenodo.10814141R, C++, Chttps://joss.theoj.org/papers/10.21105/joss.06321.pdftopological data analysis, persistent homologytag:joss.theoj.org,2005:Paper/48242024-03-18T12:36:12Z2024-03-19T00:01:19ZShapelets: A Python package implementing shapelet functions and their applicationsacceptedv0.12023-09-30 00:32:46 UTC952024-03-18 12:36:12 UTC920246058MatthewPeresTinoDepartment of Chemical Engineering, University of Waterloo, Ontario, Canada0009-0005-6832-1761AbbasYusufAbdulazizDepartment of Chemical Engineering, University of Waterloo, Ontario, CanadaRobertSudermanGoogle Inc.ThomasAkdenizEast Coast Asset Management SEZCNasserMohieddinAbukhdeirDepartment of Chemical Engineering, University of Waterloo, Ontario, Canada, Department of Physics and Astronomy, University of Waterloo, Ontario, Canada, Waterloo Institute for Nanotechnology, University of Waterloo, Ontario, Canada0000-0002-1772-037610.21105/joss.06058https://doi.org/10.5281/zenodo.10819578Pythonhttps://joss.theoj.org/papers/10.21105/joss.06058.pdfshapelets, self-assembly, astronomy, image processing, machine learningtag:joss.theoj.org,2005:Paper/48542024-03-18T12:28:23Z2024-03-19T00:01:17ZImbalance: A comprehensive multi-interface Julia toolbox to address class imbalanceacceptedv0.1.22023-10-17 14:48:00 UTC952024-03-18 12:28:23 UTC920246310EssamWisamCairo University, Egypt0009-0009-1198-7166AnthonyBlaomUniversity of Auckland, New Zealand0000-0001-6689-886X10.21105/joss.06310https://doi.org/10.5281/zenodo.10823254Juliahttps://joss.theoj.org/papers/10.21105/joss.06310.pdfmachine learning, classification, class imbalance, resampling, oversampling, undersampling, juliatag:joss.theoj.org,2005:Paper/46832024-03-09T08:42:01Z2024-03-10T00:01:18ZPerMetrics: A Framework of Performance Metrics for Machine Learning Modelsacceptedv1.4.22023-08-08 05:27:41 UTC952024-03-09 08:42:01 UTC920246143NguyenVan ThieuFaculty of Computer Science, Phenikaa University, Yen Nghia, Ha Dong, Hanoi, 12116, Vietnam.0000-0001-9994-874710.21105/joss.06143https://doi.org/10.5281/zenodo.3951205Pythonhttps://joss.theoj.org/papers/10.21105/joss.06143.pdfmodel assessment tools, performance metrics, classification validation metrics, regression evaluation criteria, clustering criterion indices, machine learning metricstag:joss.theoj.org,2005:Paper/44372024-03-08T22:12:15Z2024-03-10T08:11:09ZEthome: tools for machine learning of animal behavioracceptedv0.6.02023-04-18 06:25:31 UTC952024-03-08 22:12:15 UTC920245623BenjaminLansdellDevelopmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, United States of America0000-0003-1444-1950AbbasShirinifardDevelopmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, United States of America10.21105/joss.05623https://doi.org/10.5281/zenodo.10680136Python, Rubyhttps://joss.theoj.org/papers/10.21105/joss.05623.pdfsupervised-learning, deeplabcut, boris, neurodata-without-borders, pose-tracking, ndx-pose, animal-behaviortag:joss.theoj.org,2005:Paper/48582024-03-06T17:32:25Z2024-03-07T00:00:24Zcalorine: A Python package for constructing and sampling neuroevolution potential modelsacceptedv2.02023-10-21 15:02:27 UTC952024-03-06 17:32:25 UTC920246264EricLindgrenDepartment of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden0000-0002-8549-6839MagnusRahmDepartment of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden0000-0002-6777-0371ErikFranssonDepartment of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden0000-0001-5262-3339FredrikErikssonDepartment of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden0000-0002-7945-5483NicklasÖsterbackaDepartment of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden0000-0002-6043-4607ZheyongFanCollege of Physical Science and Technology, Bohai University, Jinzhou 121013, P. R. China0000-0002-2253-8210PaulErhartDepartment of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden0000-0002-2516-606110.21105/joss.06264https://doi.org/10.5281/zenodo.10723374Python, C++, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.06264.pdfcondensed matter, machine learning, interatomic potentials, force fields, molecular dynamics, neuroevolution, neural networktag:joss.theoj.org,2005:Paper/46922024-03-01T08:15:12Z2024-03-05T12:14:06ZΦ-ML: Intuitive Scientific Computing with Dimension Types for Jax, PyTorch, TensorFlow & NumPyacceptedv1.0.02023-08-11 11:05:07 UTC952024-03-01 08:15:12 UTC920246171PhilippHollSchool of Computation, Information and Technology, Technical University of Munich, Germany0000-0001-9246-5195NilsThuereySchool of Computation, Information and Technology, Technical University of Munich, Germany0000-0001-6647-891010.21105/joss.06171https://doi.org/10.6084/m9.figshare.25282300Python, C++https://joss.theoj.org/papers/10.21105/joss.06171.pdfMachine Learning, Jax, TensorFlow, PyTorch, NumPy, Differentiable simulations, Sparse linear systems, Preconditionerstag:joss.theoj.org,2005:Paper/45382024-02-29T10:31:27Z2024-03-01T17:39:01ZPyBADS: Fast and robust black-box optimization in Pythonacceptedv1.0.02023-06-11 13:10:30 UTC942024-02-29 10:31:27 UTC920245694GurjeetSangraSinghUniversity of Geneva, University of Applied Sciences and Arts Western Switzerland (HES-SO)0009-0008-2340-5867LuigiAcerbiUniversity of Helsinki0000-0001-7471-733610.21105/joss.05694https://doi.org/10.5281/zenodo.10696782Pythonhttps://joss.theoj.org/papers/10.21105/joss.05694.pdfBayesian optimization, Black-box optimization, Optimization, Machine learning, Gaussian Processestag:joss.theoj.org,2005:Paper/48482024-02-29T09:57:40Z2024-03-01T00:00:27ZSurjectors: surjection layers for density estimation with normalizing flowsacceptedv0.0.32023-10-14 23:34:59 UTC942024-02-29 09:57:40 UTC920246188SimonDirmeierSwiss Data Science Center, Zurich, Switzerland, ETH Zurich, Zurich, Switzerland10.21105/joss.06188https://doi.org/10.5281/zenodo.10679869Pythonhttps://joss.theoj.org/papers/10.21105/joss.06188.pdfJAX, Density estimation, Normalizing flow, Machine learning, Statisticstag:joss.theoj.org,2005:Paper/51202024-02-27T22:28:57Z2024-03-08T22:12:44ZLobsterPy: A package to automatically analyze LOBSTER runsacceptedv0.3.62024-01-19 14:16:48 UTC942024-02-27 22:28:57 UTC920246286AakashAshokNaikFederal Institute for Materials Research and Testing, Materials Chemistry Department, Berlin, 12205, Germany, Friedrich Schiller University Jena, Institute of Condensed Matter Theory and Solid-State Optics, Jena, 07743, Germany0000-0002-6071-6786KatharinaUeltzenFederal Institute for Materials Research and Testing, Materials Chemistry Department, Berlin, 12205, Germany0009-0003-2967-1182ChristinaErturalFederal Institute for Materials Research and Testing, Materials Chemistry Department, Berlin, 12205, Germany0000-0002-7696-5824AdamJ.JacksonScientific Computing Department, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Didcot, OX11 0QX, UK0000-0001-5272-6530JanineGeorgeFederal Institute for Materials Research and Testing, Materials Chemistry Department, Berlin, 12205, Germany, Friedrich Schiller University Jena, Institute of Condensed Matter Theory and Solid-State Optics, Jena, 07743, Germany0000-0001-8907-033610.21105/joss.06286https://doi.org/10.5281/zenodo.10713348Pythonhttps://joss.theoj.org/papers/10.21105/joss.06286.pdfAutomation, Bonding analysis, Machine learning