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
    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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  • 4
    DBNL

    DBNL

    Dynamic Bayesian Network Library

    ...It allows you to create simple static networks as well as complex temporal models with changing structure. It can handle highly non-linear dependencies between multivariate random variables. The particle based inference can answer arbitrary questions given the provided evidence and can even cope with multimodal densities. The library supports the most common types of densities and conditional densities, like uniform or normal densities and facilitates user defined density functions. To enable easy use the library is taking account of modern development techniques like policy based design and template programming. ...
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