4 projects for "decomposition" with 2 filters applied:

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  • 1
    mapcn

    mapcn

    Beautiful map components, 100% Free, Zero config, one command setup

    ...The core idea is to enable agents to generalize learned behavior by representing continuous control policies in a compact, interpretable form that preserves smoothness and controllability. The project includes implementations that experiment with policy encoding, action decomposition techniques, and sample efficiency analysis in classic reinforcement learning environments. By organizing action outputs into meaningful, lower-dimensional manifolds, MapCN attempts to improve both learning convergence and transfer performance when compared to unguided continuous control baselines.
    Downloads: 3 This Week
    Last Update:
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  • 2
    Universal Java Matrix Package

    Universal Java Matrix Package

    sparse and dense matrix, linear algebra, visualization, big data

    The Universal Java Matrix Package (UJMP) is an open source Java library which provides sparse and dense matrix classes, as well as a large number of calculations for linear algebra such as matrix multiplication or matrix inverse. Operations such as mean, correlation, standard deviation, replacement of missing values or the calculation of mutual information are supported, too. The Universal Java Matrix Package provides various visualization methods, import and export filters for a large...
    Downloads: 6 This Week
    Last Update:
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  • 3
    Qi - Quantitative Imaging (PIV and more)

    Qi - Quantitative Imaging (PIV and more)

    A collection of MATLAB tools for PIV, PTV, particle sizing, and more.

    ...Our Particle Image Velocimetry (PIV) tool, prana, implements a Robust Phase Correlation kernel for PIV analysis, and now includes Particle Tracking Velocimetry and sizing tools. Tools for pressure calculation, proper orthogonal decomposition (POD), and 3d imaging are under development.
    Downloads: 1 This Week
    Last Update:
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  • 4
    ...It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (small-world, scale-free, exponential random graphs, Fiedler random fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition.
    Downloads: 1 This Week
    Last Update:
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