tag:joss.theoj.org,2005:/papers/tagged/benchmarkingJournal of Open Source Software2023-10-30T21:01:26ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/45872023-10-30T21:01:26Z2023-10-31T20:12:32ZUQTestFuns: A Python3 library of uncertainty quantification (UQ) test functionsacceptedv0.4.02023-07-07 17:24:02 UTC902023-10-30 21:01:26 UTC820235671DamarWicaksonoCenter for Advanced Systems Understanding (CASUS) - Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany0000-0001-8587-7730MichaelHechtCenter for Advanced Systems Understanding (CASUS) - Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany0000-0001-9214-825310.21105/joss.05671https://doi.org/10.5281/zenodo.10047512Pythonhttps://joss.theoj.org/papers/10.21105/joss.05671.pdftest functions, benchmark, uncertainty quantification, metamodeling, surrogate modeling, sensitivity analysis, reliability analysis, rare event estimationtag:joss.theoj.org,2005:Paper/47242023-10-30T14:04:21Z2023-10-31T00:00:33ZJetNet: A Python package for accessing open datasets and benchmarking machine learning methods in high energy physicsacceptedv0.2.32023-08-29 16:02:38 UTC902023-10-30 14:04:21 UTC820235789RaghavKansalUC San Diego, USA, Fermilab, USA0000-0003-2445-1060CarlosParejaUC San Diego, USA0000-0002-9022-2349ZichunHaoCalifornia Institute of Technology, USA0000-0002-5624-4907JavierDuarteUC San Diego, USA0000-0002-5076-709610.21105/joss.05789https://doi.org/10.5281/zenodo.10044601Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.05789.pdfPyTorch, high energy physics, machine learning, jetstag:joss.theoj.org,2005:Paper/41862023-08-23T22:29:41Z2023-08-24T00:01:18Zctbench - compile-time benchmarking and analysisaccepted1.2.12023-02-01 11:07:45 UTC882023-08-23 22:29:41 UTC820235165JulesPenuchotUniversité Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400, Orsay, France0000-0002-6377-6880JoelFalcouUniversité Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400, Orsay, France0000-0001-5380-737510.21105/joss.05165https://doi.org/10.5281/zenodo.8270239C++https://joss.theoj.org/papers/10.21105/joss.05165.pdfmetaprogramming, compilation, benchmarking, librarytag:joss.theoj.org,2005:Paper/34332022-12-15T20:02:32Z2022-12-16T00:01:07ZDIANNA: Deep Insight And Neural Network Analysisacceptedv.0.4.02022-03-22 16:05:29 UTC802022-12-15 20:02:32 UTC720224493ElenaRanguelovaNetherlands eScience Center, Amsterdam, the Netherlands0000-0002-9834-1756ChristiaanMeijerNetherlands eScience Center, Amsterdam, the Netherlands0000-0002-5529-5761LeonOostrumNetherlands eScience Center, Amsterdam, the Netherlands0000-0001-8724-8372YangLiuNetherlands eScience Center, Amsterdam, the Netherlands0000-0002-1966-8460PatrickBosNetherlands eScience Center, Amsterdam, the Netherlands0000-0002-6033-960XGiuliaCrocioniNetherlands eScience Center, Amsterdam, the Netherlands0000-0002-0823-0121MatthieuLaneuvilleSURF, Amsterdam, the Netherlands0000-0001-6022-0046BryanCardenasGuevaraSURF, Amsterdam, the Netherlands0000-0001-9793-910XRenaBakhshiNetherlands eScience Center, Amsterdam, the Netherlands0000-0002-2932-3028DamianPodareanuSURF, Amsterdam, the Netherlands0000-0002-4207-872510.21105/joss.04493https://doi.org/10.5281/zenodo.7387004Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.04493.pdfexplainable AI, Deep Neural Networks, ONNX, benchmark datasetstag:joss.theoj.org,2005:Paper/37122022-11-23T18:54:13Z2022-11-25T13:54:46Zfseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithmsacceptedv3.0.22022-07-11 12:22:23 UTC792022-11-23 18:54:13 UTC720224611Jeroen G. S.OverschieBernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, P.O. Box 407, 9700 AK Groningen, The Netherlands0000-0003-3304-3800AhmadAlsahafDepartment of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands0000-0002-0770-1390GeorgeAzzopardiBernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, P.O. Box 407, 9700 AK Groningen, The Netherlands0000-0001-6552-259610.21105/joss.04611https://doi.org/10.5281/zenodo.7343417https://joss.theoj.org/papers/10.21105/joss.04611.pdffeature ranking, feature selection, benchmarking, machine learning, open-source, software, pythontag:joss.theoj.org,2005:Paper/36982022-11-03T14:14:40Z2022-11-04T00:01:25ZBlack-it: A Ready-to-Use and Easy-to-Extend Calibration Kit for Agent-based Modelsacceptedv0.1.12022-07-01 17:32:40 UTC792022-11-03 14:14:40 UTC720224622MarcoBenedettiBanca d'Italia, ItalyGennaroCatapanoBanca d'Italia, ItalyFrancescoDe SclavisBanca d'Italia, ItalyMarcoFavoritoBanca d'Italia, Italy0000-0001-9566-3576AldoGlielmoBanca d'Italia, Italy0000-0002-4737-2878DavideMagnanimiBanca d'Italia, Italy0000-0002-6560-8047AntonioMuciBanca d'Italia, Italy10.21105/joss.04622https://doi.org/10.5281/zenodo.7273564Pythonhttps://joss.theoj.org/papers/10.21105/joss.04622.pdfagent-based models, calibration, benchmarking, computational economicstag:joss.theoj.org,2005:Paper/36612022-11-02T16:49:06Z2022-11-05T11:34:44ZDBMS-Benchmarker: Benchmark and Evaluate DBMS in Pythonacceptedv0.11.222022-06-17 07:59:54 UTC792022-11-02 16:49:06 UTC720224628PatrickK.ErdeltBerliner Hochschule für Technik (BHT)0000-0002-3359-2386JaschaJestelBerliner Hochschule für Technik (BHT)10.21105/joss.04628https://doi.org/10.5281/zenodo.7213676Jupyter Notebook, Pythonhttps://joss.theoj.org/papers/10.21105/joss.04628.pdfDBMS, JDBCtag:joss.theoj.org,2005:Paper/25322021-08-31T18:25:45Z2021-09-01T00:01:21ZSynch: A framework for concurrent data-structures and benchmarksacceptedv2.3.12021-03-22 09:39:00 UTC642021-08-31 18:25:45 UTC620213143NikolaosD.KallimanisInstitute of Computer Science - Foundation for Research and Technology-Hellas (FORTH-ICS)0000-0002-0331-147510.21105/joss.03143https://doi.org/10.5281/zenodo.5347958C, Objective-C, C++https://joss.theoj.org/papers/10.21105/joss.03143.pdfConcurrent data-structures, benchmarks, queues, stacks, combining-objects, hash-tables, locks, wait-free, lock-free, performance evaluationtag: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/14442020-08-25T06:55:13Z2021-02-15T11:31:08ZMF2: A Collection of Multi-Fidelity Benchmark Functions in Pythonaccepted2019.11.32020-01-17 22:00:51 UTC522020-08-25 06:55:13 UTC520202049Sandervan RijnLeiden University, The Netherlands0000-0001-6159-041XSebastianSchmittHonda Research Institute Europe, Germany10.21105/joss.02049https://doi.org/10.5281/zenodo.3998591Pythonhttps://joss.theoj.org/papers/10.21105/joss.02049.pdfoptimization, benchmarks