Showing 13 open source projects for "artificial intelligence algorithm"

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  • 1
    wav2letter++

    wav2letter++

    Facebook AI research's automatic speech recognition toolkit

    First, install Flashlight (using the 0.3 branch is required) with the ASR application. This repository includes recipes to reproduce the following research papers as well as pre-trained models. All results reproduction must use Flashlight <= 0.3.2 for exact reproducibility. At least one of LZMA, BZip2, or Z is required for LM compression with KenLM. It is highly recommended to build KenLM with position-independent code (-fPIC) enabled, to enable python compatibility. After installing, run...
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  • 2
    DaNNet

    DaNNet

    Deep Artificial Neural Network framework using Armadillo

    DaNNet is a C++ deep neural network library using the Armadillo library as a base. It is intended to be a small and easy to use framework with no other dependencies than Armadillo. It uses independent layer-wise optimization giving you full flexibility to train your network.
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  • 3
    faif

    faif

    C++ header only library with AI and bioinformatics algorithms

    C++ header only library, small and fast; Naive Bayesian Classifier, Decision Tree Classifier (ID3), DNA/RNA nucleotide second structure predictor, timeseries management, timeseries prediction, generic Evolutionary Algorithm, generic Hill Climbing algorithm and others.
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  • 4

    ArgSemSAT

    Solving argumentation problems in Dung's AFs

    ...The new hosting is at https://github.com/federicocerutti/ArgSemSAT In particular, the source code for the publication: Federico Cerutti, Massimiliano Giacomin, Mauro Vallati, How we designed winning algorithms for abstract argumentation and which insight we attained, Artificial Intelligence, Volume 276, 2019, Pages 1-40, ISSN 0004-3702, https://doi.org/10.1016/j.artint.2019.08.001. (http://www.sciencedirect.com/science/article/pii/S0004370218302650) is available at https://github.com/federicocerutti/ArgSemSAT/releases/tag/1.0.5 Solving argumentation problems in Dung's AFs. Authors: Federico Cerutti <federico.cerutti@acm.org> Mauro Vallati <m.vallati@hud.ac.uk> Massimiliano Giacomin <massimiliano.giacomin@unibs.it> Credits for porting to MacOSX: Roman Kutlak <r.kutlak@abdn.ac.uk>
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  • 5

    CvHMM

    Discrete Hidden Markov Models based on OpenCV

    This project (CvHMM) is an implementation of discrete Hidden Markov Models (HMM) based on OpenCV. It is simple to understand and simple to use. The Zip file contains one header for the implementation and one main.cpp file for a demonstration of how it works. Hope it becomes useful for your projects.
    Downloads: 0 This Week
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  • 6

    ACOptim

    Ant Colony Optimizer

    Simple POSIX multithread Ant Colony Optimizer framework currently for solving Vehicle Routing Problem. Supports UDP, file synchronizatoin between computers.
    Downloads: 0 This Week
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  • 7
    An open source face recognition project.
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  • 8
    Simple C/C++ library to study and to construct an artifficial neuro networks.
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  • 9
    C/C++ steering behaviour library (AI) with Lua scripting for games and simulations.
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  • 10
    Atlantis Poker is a poker library for creating poker software. The library is being used as the backend foundation for a single player poker game.
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  • 11
    Librarian is intelligent text classification system. That provide you with the access to your electronic document based on a hierarchical system of classifications.
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  • 12
    Data Mining Platform is a platform for data mining and analysis. It contains many of the new and sophisticated methods such as kernel-based classification, two-way clustering, bayesian networks, pattern recognition for time series analysis and many other
    Downloads: 0 This Week
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  • 13
    DBNL

    DBNL

    Dynamic Bayesian Network Library

    DBNL is a cross-platform library that offers a variety of implementations of Bayesian networks and machine learning algorithms. It is a flexible library that covers all aspects of Bayesian netwoks from representation to reasoning and learning. 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...
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