tag:joss.theoj.org,2005:/papers/tagged/jaxJournal of Open Source Software2024-03-01T08:15:12ZJournal of Open Source Softwarehttps://joss.theoj.orgtag: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/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/45312023-09-15T22:14:43Z2023-09-16T00:01:22Zpysersic: A Python package for determining galaxy structural properties via Bayesian inference, accelerated with jaxacceptedv0.1.12023-06-07 14:31:39 UTC892023-09-15 22:14:43 UTC820235703ImadPashaDepartment of Astronomy, Yale University, USA, National Science Foundation Graduate Research Fellow0000-0002-7075-9931TimB.MillerDepartment of Astronomy, Yale University, USA0000-0001-8367-626510.21105/joss.05703https://doi.org/10.5281/zenodo.8335352https://joss.theoj.org/papers/10.21105/joss.05703.pdfPython, astronomy, galaxies, model fittingtag:joss.theoj.org,2005:Paper/43712023-09-12T14:28:53Z2023-09-13T00:01:17Zqujax: Simulating quantum circuits with JAXacceptedv0.3.42023-03-29 14:28:50 UTC892023-09-12 14:28:53 UTC820235504SamuelDuffieldQuantinuum0000-0002-8656-8734GabrielMatosQuantinuum, University of Leeds0000-0002-3373-0128MelfJohannsenQuantinuum10.21105/joss.05504https://doi.org/10.5281/zenodo.8268973Pythonhttps://joss.theoj.org/papers/10.21105/joss.05504.pdfJAX, quantum computationtag:joss.theoj.org,2005:Paper/39952023-03-09T21:00:11Z2023-03-10T00:04:43ZflowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAXacceptedv0.0.72022-11-10 08:41:32 UTC832023-03-09 21:00:11 UTC820235021KazeW. k.WongCenter for Computational Astrophysics, Flatiron Institute, New York, NY 10010, US0000-0001-8432-7788MarylouGabriéÉcole Polytechnique, Palaiseau 91120, France, Center for Computational Mathematics, Flatiron Institute, New York, NY 10010, US0000-0002-5989-1018DanielForeman-MackeyCenter for Computational Astrophysics, Flatiron Institute, New York, NY 10010, US0000-0002-9328-565210.21105/joss.05021https://doi.org/10.5281/zenodo.7706605Pythonhttps://joss.theoj.org/papers/10.21105/joss.05021.pdfBayesian Inference, Machine Learning, JAXtag:joss.theoj.org,2005:Paper/39472023-01-30T21:54:26Z2023-01-31T10:47:41ZPyKronecker: A Python Library for the Efficient Manipulation of Kronecker Products and Related Structuresaccepted0.1.12022-10-07 09:14:34 UTC812023-01-30 21:54:26 UTC820234900EdwardAntonianHeriot-Watt University, United KingdomGarethW.PetersUniversity of California Santa Barbara, United States of AmericaMichaelChantlerHeriot-Watt University, United Kingdom10.21105/joss.04900https://doi.org/10.5281/zenodo.7566803Pythonhttps://joss.theoj.org/papers/10.21105/joss.04900.pdfNumpy, Jax, Kronecker product, Kronecker sum, linear system, GPUtag:joss.theoj.org,2005:Paper/36252022-07-26T20:35:08Z2022-07-27T00:00:31ZGPJax: A Gaussian Process Framework in JAXacceptedv0.4.52022-05-24 16:24:45 UTC752022-07-26 20:35:08 UTC720224455ThomasPinderDepartment of Mathematics and Statistics, Lancaster University, United KingdomDanielDoddSTOR-i Centre for Doctoral Training, Lancaster University, United Kingdom10.21105/joss.04455https://doi.org/10.5281/zenodo.6882220Pythonhttps://joss.theoj.org/papers/10.21105/joss.04455.pdfGaussian processes, JAX, Bayesian inference, Machine learning, Statisticstag:joss.theoj.org,2005:Paper/32932022-04-18T15:53:10Z2022-04-19T00:00:54ZSGMCMCJax: a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithmsaccepted0.2.92022-01-17 18:25:10 UTC722022-04-18 15:53:10 UTC720224113JeremieCoullonCervest, London, UK0000-0002-7032-3425ChristopherNemethLancaster University, UK0000-0002-9084-386610.21105/joss.04113https://doi.org/10.5281/zenodo.6460681Pythonhttps://joss.theoj.org/papers/10.21105/joss.04113.pdfJAX, MCMC, SGMCMC, Bayesian inferencetag:joss.theoj.org,2005:Paper/31172022-01-14T14:04:16Z2022-01-15T00:01:27ZcompareMCMCs: An R package for studying MCMC efficiencyaccepted0.5.02021-10-18 20:34:22 UTC692022-01-14 14:04:16 UTC720223844Perryde ValpineUniversity of California, BerkeleySallyPaganinUniversity of California, BerkeleyDanielTurekWilliams College10.21105/joss.03844https://doi.org/10.5281/zenodo.5842623Rhttps://joss.theoj.org/papers/10.21105/joss.03844.pdfstatistics, Markov chain Monte Carlo, nimble, JAGStag:joss.theoj.org,2005:Paper/31672021-12-02T19:14:54Z2021-12-03T00:01:30ZCR-Sparse: Hardware accelerated functional algorithms for sparse signal processing in Python using JAXacceptedv0.2.12021-11-12 03:40:56 UTC682021-12-02 19:14:54 UTC620213917ShaileshKumarIndian Institute of Technology, Delhi0000-0003-2217-476810.21105/joss.03917https://doi.org/10.5281/zenodo.5749792Pythonhttps://joss.theoj.org/papers/10.21105/joss.03917.pdfsparse and redundant representations, compressive sensing, wavelets, linear operators, sparse subspace clustering, functional programming