Showing 4 open source projects for "blackbox for windows"

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  • 1
    Open Source Vizier

    Open Source Vizier

    Python-based research interface for blackbox

    Open Source (OSS) Vizier is a Python-based interface for blackbox optimization and research, based on Google’s original internal Vizier, one of the first hyperparameter tuning services designed to work at scale. Allows a user to setup an OSS Vizier Server, which can host black-box optimization algorithms to serve multiple clients simultaneously in a fault-tolerant manner to tune their objective functions. Defines abstractions and utilities for implementing new optimization algorithms for...
    Downloads: 0 This Week
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  • 2
    ClusterFuzz

    ClusterFuzz

    Scalable fuzzing infrastructure

    ClusterFuzz is a scalable fuzzing infrastructure that finds security and stability issues in software. Google uses ClusterFuzz to fuzz all Google products and as the fuzzing backend for OSS-Fuzz. ClusterFuzz provides many features which help seamlessly integrate fuzzing into a software project's development process. Can run on any size cluster (e.g. OSS-Fuzz instance runs on 100,000 VMs). Fully automatic bug filing, triage and closing for various issue trackers (e.g. Monorail, Jira)....
    Downloads: 2 This Week
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  • 3
    Interpret-Text

    Interpret-Text

    State-of-the-art explainers for text-based machine learning models

    A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard. Interpret-Text builds on Interpret, an open source python package for training interpretable models and helping to explain blackbox machine learning systems. We have added extensions to support text models. Interpret-Text incorporates community-developed interpretability techniques for NLP models and a visualization dashboard to view the results....
    Downloads: 0 This Week
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  • 4
    The QTaste is an open-source functional and non-functional test environment offering services to developers and testers. It has been implemented following a data driven testing philosophy. (WE MOVED TO GITHUB https://github.com/qspin/qtaste) Continuous Integration available at https://qtaste.ci.cloudbees.com/
    Downloads: 0 This Week
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