tag:joss.theoj.org,2005:/papers/reviewed_by/@terrytangyuanJournal of Open Source Software2021-10-19T07:22:28ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/16912021-10-19T07:22:28Z2021-10-20T00:03:24ZTorchGAN: A Flexible Framework for GAN Training and Evaluationacceptedv0.0.42020-05-26 23:46:08 UTC662021-10-19 07:22:28 UTC620212606AvikPalIndian Institute of Technology Kanpur0000-0002-3938-7375AniketDasIndian Institute of Technology Kanpur10.21105/joss.02606https://doi.org/10.5281/zenodo.5575758Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.02606.pdfDeep Learning, Machine Learning, Generative Adversarial Networks, Unsupervised Learning, Computer Vision, Generative Modelstag:joss.theoj.org,2005:Paper/10272019-09-04T15:48:59Z2021-02-15T11:32:06ZSmartEDA: An R Package for Automated Exploratory Data Analysisacceptedv0.3.12019-06-04 10:12:20 UTC412019-09-04 15:48:59 UTC420191509SayanPutatundaVMware Software India Pvt ltd.0000-0002-6477-0376DayanandaUbrangalaVMware Software India Pvt ltd.KiranRamaVMware Software India Pvt ltd.RaviKondapalliVMware Software India Pvt ltd.10.21105/joss.01509https://doi.org/10.5281/zenodo.3383824Rhttps://joss.theoj.org/papers/10.21105/joss.01509.pdfExploratory Data Analysis, Data Miningtag:joss.theoj.org,2005:Paper/9932019-06-27T06:23:27Z2021-02-15T11:32:12ZmodelDown: automated website generator with interpretable documentation for predictive machine learning modelsaccepted1.0.02019-05-08 20:51:38 UTC382019-06-27 06:23:27 UTC420191444KamilRomaszkoFaculty of Mathematics and Information Science, Warsaw University of TechnologyMagdaTatarynowiczFaculty of Mathematics and Information Science, Warsaw University of TechnologyMateuszUrbańskiFaculty of Mathematics and Information Science, Warsaw University of TechnologyPrzemysławBiecekFaculty of Mathematics and Information Science, Warsaw University of Technology0000-0001-8423-182310.21105/joss.01444https://doi.org/10.5281/zenodo.3247303Rhttps://joss.theoj.org/papers/10.21105/joss.01444.pdfexplainable artificial intelligence, interpretable machine learning, predictive models, model visualization, automated data analysistag:joss.theoj.org,2005:Paper/7052019-06-12T16:15:44Z2021-02-19T14:34:34ZReinforcementLearning: A Package to Perform Model-Free Reinforcement Learning in Racceptedv1.0.22018-10-22 17:31:07 UTC382019-06-12 16:15:44 UTC420191087NicolasPröellochsUniversity of Giessen, University of OxfordStefanFeuerriegelETH Zurich10.21105/joss.01087https://doi.org/10.5281/zenodo.3244170Rhttps://joss.theoj.org/papers/10.21105/joss.01087.pdfReinforcement Learning, Batch Learning, Experience Replay, Q-Learningtag:joss.theoj.org,2005:Paper/4942019-01-15T20:32:49Z2021-02-15T11:33:23ZNN-SVG: Publication-Ready Neural Network Architecture Schematicsaccepted1.0.02018-05-13 13:04:48 UTC332019-01-15 20:32:49 UTC42019747AlexanderLeNailMassachusetts Institute of Technology, dept of Biological Engineering0000-0001-8173-231510.21105/joss.00747https://doi.org/10.5281/zenodo.2541121JavaScripthttps://joss.theoj.org/papers/10.21105/joss.00747.pdfmachine learning, deep learning, neural networks, visualizationtag:joss.theoj.org,2005:Paper/6642018-12-10T18:50:20Z2021-02-15T11:32:53ZAltair: Interactive Statistical Visualizations for Pythonacceptedv22018-10-15 22:36:16 UTC322018-12-10 18:50:20 UTC320181057JacobVanderPlasUniversity of Washington0000-0002-9623-3401BrianE.GrangerCalifornia Polytechnic State University, San Luis Obispo0000-0002-5223-6168JeffreyHeerUniversity of Washington0000-0002-6175-1655DominikMoritzUniversity of Washington0000-0002-3110-1053KanitWongsuphasawatUniversity of Washington0000-0001-7231-279XArvindSatyanarayanMIT CSAIL0000-0001-5564-635XEitanLeesFlorida State University0000-0003-0988-6015IliaTimofeevTTS Consulting0000-0003-1795-943XBenWelshLos Angeles Times Data Desk0000-0002-5200-7269ScottSievertUniversity of Wisconsin--Madison0000-0002-4275-345210.21105/joss.01057https://doi.org/10.5281/zenodo.2030098Pythonhttps://joss.theoj.org/papers/10.21105/joss.01057.pdfvisualization, statistics, Jupytertag:joss.theoj.org,2005:Paper/5552018-09-02T20:57:24Z2021-02-15T11:33:12ZUMAP: Uniform Manifold Approximation and Projectionacceptedv0.3.02018-07-19 01:13:25 UTC292018-09-02 20:57:24 UTC32018861LelandMcInnesTutte Institute for Mathematics and Computing0000-0003-2143-6834JohnHealyTutte Institute for Mathematics and ComputingNathanielSaulDepartment of Mathematics and Statistics, Washington State UniversityLukasGroßbergerErnst Strüngmann Institute for Neuroscience in cooperation with Max Planck Society, Donders Institute for Brain, Cognition and Behaviour, Radboud Universiteit10.21105/joss.00861https://doi.org/10.5281/zenodo.1407268Pythonhttps://joss.theoj.org/papers/10.21105/joss.00861.pdfmanifold learning, dimension reduction, unsupervised learning