Showing 5 open source projects for "inference"

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  • 1
    Catalyst

    Catalyst

    Accelerated deep learning R&D

    Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make...
    Downloads: 1 This Week
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  • 2
    Ceka

    Ceka

    Crowd Environment and its Knowledge Analysis

    A knowledge analysis tool for crowdsourcing based on Weka. We also have a Python version of Crowdsourcing Learning: CrowdwiseKit on GitHub (https://github.com/tssai-lab/CrowdwiseKit).
    Downloads: 5 This Week
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  • 3
    GUAJE FUZZY

    GUAJE FUZZY

    Free software for generating understandable and accurate fuzzy systems

    GUAJE stands for Generating Understandable and Accurate fuzzy models in a Java Environment. Thus, it is a free software tool (licensed under GPL-v3) with the aim of supporting the design of interpretable and accurate fuzzy systems by means of combining several preexisting open source tools, taking profit from the main advantages of all of them. It is a user-friendly portable tool designed and developed in order to make easier knowledge extraction and representation for fuzzy systems, paying...
    Downloads: 0 This Week
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  • 4
    A framework for solving large systems of linear equations (Ax=b) in a distributed network using different iterative methods. The solver is written on Java, can be run on Windows or Linux. The solver is compatible with either MPICH2 or mpj-express.
    Downloads: 0 This Week
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  • 5
    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. ...
    Downloads: 0 This Week
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