tag:joss.theoj.org,2005:/papers/tagged/Machine%20Learning?page=3Journal of Open Source Software2023-11-05T15:58:05ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/42122023-11-05T15:58:05Z2023-11-06T00:00:26ZSuperNOVA: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in Racceptedv1.0.02023-02-15 18:37:21 UTC912023-11-05 15:58:05 UTC820235422DavidMcCoyDepartment of Biostatistics, University of California Berkeley, Berkeley, CA 94704, U.S.A.0000-0002-5515-6307AlejandroSchulerDepartment of Biostatistics, University of California Berkeley, Berkeley, CA 94704, U.S.A.0000-0003-4853-6130AlanHubbardDepartment of Biostatistics, University of California Berkeley, Berkeley, CA 94704, U.S.A.0000-0002-3769-0127Markvan der LaanDepartment of Biostatistics, University of California Berkeley, Berkeley, CA 94704, U.S.A.0000-0003-1432-551110.21105/joss.05422https://doi.org/10.5281/zenodo.10038794Rhttps://joss.theoj.org/papers/10.21105/joss.05422.pdfcausal inference, machine learning, stochastic interventions, efficient estimation, targeted learning, mixed exposurestag: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/45852023-10-12T07:00:05Z2023-10-13T00:01:03ZSIRUS.jl: Interpretable Machine Learning via Rule Extractionacceptedv1.2.12023-07-07 12:46:28 UTC902023-10-12 07:00:05 UTC820235786RikHuijzerUniversity of Groningen, Groningen, the Netherlands0000-0001-9445-8466FrankBlaauwResearchable, Assen, the Netherlands0000-0002-6588-5079RuudJ.R. denHartighUniversity of Groningen, Groningen, the Netherlands0000-0002-0094-830710.21105/joss.05786https://doi.org/10.5281/zenodo.8398350Juliahttps://joss.theoj.org/papers/10.21105/joss.05786.pdftag:joss.theoj.org,2005:Paper/43782023-10-09T09:55:13Z2023-10-10T00:00:57ZWSKNN - Weighted Session-based K-NN recommender systemaccepted1.12023-03-31 15:24:58 UTC902023-10-09 09:55:13 UTC820235639SzymonMolińskiSales Intelligence Sp. z o.o., Digitree SA0000-0003-3525-210410.21105/joss.05639https://doi.org/10.5281/zenodo.8414247Jupyter Notebook, Perl, Pythonhttps://joss.theoj.org/papers/10.21105/joss.05639.pdfmachine learning, e-commerce, recommender system, recommender enginetag:joss.theoj.org,2005:Paper/40712023-10-08T07:45:43Z2023-10-09T09:21:20Zbrains-py, A framework to support research on energy-efficient unconventional hardware for machine learningaccepted1.0.22022-12-04 18:53:21 UTC902023-10-08 07:45:43 UTC820235573UnaiAlegre-IbarraMESA+ Institute for Nanotechnology & BRAINS Center for Brain-Inspired Nano Systems, University of Twente, Netherlands0000-0001-5957-7945Hans-ChristianRuizEulerMESA+ Institute for Nanotechnology & BRAINS Center for Brain-Inspired Nano Systems, University of Twente, NetherlandsHumaidA.MollahMESA+ Institute for Nanotechnology & BRAINS Center for Brain-Inspired Nano Systems, University of Twente, NetherlandsBozhidarP.PetrovMESA+ Institute for Nanotechnology & BRAINS Center for Brain-Inspired Nano Systems, University of Twente, NetherlandsSrikumarS.SastryMESA+ Institute for Nanotechnology & BRAINS Center for Brain-Inspired Nano Systems, University of Twente, NetherlandsMarcusN.BoonMESA+ Institute for Nanotechnology & BRAINS Center for Brain-Inspired Nano Systems, University of Twente, NetherlandsMichelP.de JongMESA+ Institute for Nanotechnology & BRAINS Center for Brain-Inspired Nano Systems, University of Twente, NetherlandsMohamadrezaZolfagharinejadMESA+ Institute for Nanotechnology & BRAINS Center for Brain-Inspired Nano Systems, University of Twente, NetherlandsFlorentinaM. j.UitzetterMESA+ Institute for Nanotechnology & BRAINS Center for Brain-Inspired Nano Systems, University of Twente, NetherlandsBramvan de VenMESA+ Institute for Nanotechnology & BRAINS Center for Brain-Inspired Nano Systems, University of Twente, NetherlandsAntónioJ. Sousade AlmeidaMESA+ Institute for Nanotechnology & BRAINS Center for Brain-Inspired Nano Systems, University of Twente, NetherlandsSachinKingeMESA+ Institute for Nanotechnology & BRAINS Center for Brain-Inspired Nano Systems, University of Twente, NetherlandsWilfredG.van der WielAdvanced Tech., Materials Engineering Div., Toyota Motor Europe, Belgium10.21105/joss.05573https://doi.org/10.5281/zenodo.8410268Pythonhttps://joss.theoj.org/papers/10.21105/joss.05573.pdfDopant network processing units (DNPUs), Material Learning, Machine Learning Hardware design, Efficient Computing, Materials Sciencetag:joss.theoj.org,2005:Paper/45652023-10-01T12:42:45Z2023-10-02T00:01:29ZPyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphsacceptedv1.3.12023-06-26 18:09:02 UTC902023-10-01 12:42:45 UTC820235713FedericoErricaNEC Laboratories Europe, Germany0000-0001-5181-2904DavideBacciuUniversity of Pisa, Italy0000-0001-5213-2468AlessioMicheliUniversity of Pisa, Italy0000-0001-5764-523810.21105/joss.05713https://doi.org/10.5281/zenodo.8396373Pythonhttps://joss.theoj.org/papers/10.21105/joss.05713.pdfMachine Learning, Graph Networks, Deep Learning for Graphstag:joss.theoj.org,2005:Paper/45352023-09-22T09:41:00Z2023-09-26T09:02:22Zpactus: A Python framework for trajectory classificationacceptedv0.4.02023-06-08 20:13:58 UTC892023-09-22 09:41:00 UTC820235738G.Viera-LópezDepartment of Computer Science, Gran Sasso Science Institute, L'Aquila, Italy0000-0002-9661-5709J.j.Morgado-VegaDepartment of Artificial Intelligence, University of Havana, Havana, Cuba0000-0001-6067-9172A.ReyesGroup of Complex Systems and Statistical Physics, University of Havana, Havana, Cuba0000-0001-7305-4710EAltshulerGroup of Complex Systems and Statistical Physics, University of Havana, Havana, Cuba0000-0003-4192-5635YudiviánAlmeida-CruzDepartment of Artificial Intelligence, University of Havana, Havana, Cuba0000-0002-2345-1387GiorgioManganiniDepartment of Computer Science, Gran Sasso Science Institute, L'Aquila, Italy0000-0002-5394-409410.21105/joss.05738https://doi.org/10.5281/zenodo.8352324Pythonhttps://joss.theoj.org/papers/10.21105/joss.05738.pdftrajectory classification, mobility data, machine learningtag:joss.theoj.org,2005:Paper/42602023-09-20T00:32:03Z2023-09-21T00:01:17ZSpikeometric: Linear Non-Linear Cascade Spiking Neural Networks with Pytorch Geometricacceptedv1.0.02023-02-23 16:23:51 UTC892023-09-20 00:32:03 UTC820235451JakobL.SønstebøDepartment of Numerical Analysis and Scientific Computing, Simula Research Laboratory, Oslo, Norway0009-0009-0584-9293HermanBrunborgDepartment of Physics, University of Oslo, Oslo, NorwayMikkelElleLepperødDepartment of Numerical Analysis and Scientific Computing, Simula Research Laboratory, Oslo, Norway, Department of Physics, University of Oslo, Oslo, Norway0000-0002-4262-554910.21105/joss.05451https://doi.org/10.5281/zenodo.8358903Pythonhttps://joss.theoj.org/papers/10.21105/joss.05451.pdfpython, computational neuroscience, machine learning, spiking neural networks, generalized linear models, linear non-linear poisson modelstag:joss.theoj.org,2005:Paper/44832023-09-14T03:02:07Z2023-09-22T03:40:20ZMLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPSacceptedv1.0.02023-05-12 19:56:27 UTC892023-09-14 03:02:07 UTC820235620PaulJ.AtzbergerPaul J. Atzberger, Professor, University of California Santa Barbara0000-0001-6806-806910.21105/joss.05620https://doi.org/10.5281/zenodo.8327516Python, C++https://joss.theoj.org/papers/10.21105/joss.05620.pdfmachine learning, dynamicstag:joss.theoj.org,2005:Paper/44872023-09-05T14:12:36Z2023-09-06T00:01:27ZMolearn: a Python package streamlining the design of generative models of biomolecular dynamicsaccepted2.0.12023-05-17 16:07:52 UTC892023-09-05 14:12:36 UTC820235523SamuelC.MussonDepartment of Physics, Durham University, United Kingdom0000-0002-2189-554XMatteoT.DegiacomiDepartment of Physics, Durham University, United Kingdom0000-0003-4672-471X10.21105/joss.05523https://doi.org/10.5281/zenodo.8284102Pythonhttps://joss.theoj.org/papers/10.21105/joss.05523.pdfmachine learning, molecular dynamics, proteins