Showing 4 open source projects for "conditional random field"

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    Best-websites-a-programmer-should-visit

    Best-websites-a-programmer-should-visit

    Some useful websites for programmers

    Best-websites-a-programmer-should-visit is a living, community-curated directory of links that programmers consistently find useful throughout their careers. Rather than being a random bookmark dump, it organizes resources into practical categories such as algorithms, competitive programming, reading materials, podcasts, newsletters, interview prep, design, security, performance, and more. The list aims to reduce the “what should I learn next?” friction by pointing you to high-signal,...
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  • 2
    Probability Cheatsheet

    Probability Cheatsheet

    A comprehensive 10-page probability cheatsheet

    The probability_cheatsheet is a cheat sheet repository that summarizes key probability theory concepts, formulas, distributions, and properties in a concise format. It likely includes definitions of random variables, PMFs and PDFs, expectations, variance, common distributions (e.g. binomial, normal, Poisson, exponential), conditional probability, Bayes’ theorem, moment generating functions, and perhaps important inequalities (Markov, Chebyshev, Chernoff). The cheat sheet is intended as a quick reference for students, data scientists, statisticians, or anyone needing to recall core probability formulas without diving into textbooks. ...
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  • 3
    Accord.NET Framework

    Accord.NET Framework

    Scientific computing, machine learning and computer vision for .NET

    The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono.
    Downloads: 1 This Week
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  • 4
    CRFasRNN

    CRFasRNN

    Semantic image segmentation method described in the ICCV 2015 paper

    CRF-RNN is a deep neural architecture that integrates fully connected Conditional Random Fields (CRFs) with Convolutional Neural Networks (CNNs) by reformulating mean-field CRF inference as a Recurrent Neural Network. This fusion enables end-to-end training via backpropagation for semantic image segmentation tasks, eliminating the need for separate, offline post-processing steps. Our work allows computers to recognize objects in images, what is distinctive about our work is that we also recover the 2D outline of objects. ...
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