Showing 4 open source projects for "generative"

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  • 1
    ProjectLibre - Project Management

    ProjectLibre - Project Management

    #1 alternative to Microsoft Project : Project Management & Gantt Chart

    ProjectLibre project management software: #1 free alternative to Microsoft Project w/ 7.8M+ downloads in 193 countries. ProjectLibre is a replacement of MS Project & includes Gantt Chart, Network Diagram, WBS, Earned Value etc. This site downloads our FOSS desktop app. 🌐 Try the Cloud: http://www.projectlibre.com/register/trial We also offer ProjectLibre Cloud—a subscription, AI-powered SaaS for teams & enterprises. Cloud supports multi-project management w/ role-based access, central...
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    Downloads: 14,308 This Week
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  • 2
    Mindolph

    Mindolph

    Mindolph is an open source desktop PKM software with Gen-AI support.

    Mindolph is an open source personal knowledge management software with Gen-AI support for all desktop platforms.
    Downloads: 2 This Week
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  • 3
    Joget

    Joget

    AI Powered Open Source Platform to Easily Build Enterprise Web Apps

    Joget offers an open-source, AI-powered platform that converges no-code/low-code development with AI to rapidly build and customize enterprise applications at scale. By combining AI with visual app builders—not raw code—Joget makes app generation faster, safer, and more accessible for everyone. With Generative AI and Agentic AI capabilities, Joget Intelligence enables organizations to automate and enhance processes while maintaining oversight and compliance. Unlike typical AI code generation, Joget's visual-first approach ensures applications are maintainable and governed within collaborative human workflows. As an Application and Integration Fabric, Joget connects legacy and modern systems seamlessly. ...
    Downloads: 6 This Week
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  • 4
    ...It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (small-world, scale-free, exponential random graphs, Fiedler random fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition.
    Downloads: 0 This Week
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