Showing 8 open source projects for "open dynamics engine"

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    FairChem

    FairChem

    FAIR Chemistry's library of machine learning methods for chemistry

    FAIRChem is a unified library for machine learning in chemistry and materials, consolidating data, pretrained models, demos, and application code into a single, versioned toolkit. Version 2 modernizes the stack with a cleaner core package and breaking changes relative to V1, focusing on simpler installs and a stable API surface for production and research. The centerpiece models (e.g., UMA variants) plug directly into the ASE ecosystem via a FAIRChem calculator, so users can run relaxations,...
    Downloads: 6 This Week
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  • 2
    Neural Tangents

    Neural Tangents

    Fast and Easy Infinite Neural Networks in Python

    Neural Tangents is a high-level neural network API for specifying complex, hierarchical models at both finite and infinite width, built in Python on top of JAX and XLA. It lets researchers define architectures from familiar building blocks—convolutions, pooling, residual connections, and nonlinearities—and obtain not only the finite network but also the corresponding Gaussian Process (GP) kernel of its infinite-width limit. With a single specification, you can compute NNGP and NTK kernels,...
    Downloads: 10 This Week
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  • 3
    Minkowski Engine

    Minkowski Engine

    Auto-diff neural network library for high-dimensional sparse tensors

    The Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unspooling, and broadcasting operations for sparse tensors. The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to...
    Downloads: 0 This Week
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  • 4
    Euler

    Euler

    A distributed graph deep learning framework.

    As a general data structure with strong expressive ability, graphs can be used to describe many problems in the real world, such as user networks in social scenarios, user and commodity networks in e-commerce scenarios, communication networks in telecom scenarios, and transaction networks in financial scenarios. and drug molecule networks in medical scenarios, etc. Data in the fields of text, speech, and images is easier to process into a grid-like type of Euclidean space, which is suitable...
    Downloads: 0 This Week
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  • 5
    Neuroph OCR - Handwriting Recognition
    Neuroph OCR - Handwriting Recognition is developed to recognize hand written letter and characters. It's engine derived's from the Java Neural Network Framework - Neuroph and as such it can be used as a standalone project or a Neuroph plug in.
    Downloads: 0 This Week
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  • 6
    Neural - is neural network engine with object-oriented design. Features: - Supports: backpropogation, RPROP algorithms. - Flexible input/outputs framework. - Distributed calculations.
    Downloads: 0 This Week
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  • 7
    NeuronDotNet is a neural network engine written in C#. It provides an interface for advanced AI programmers to design various types of artificial neural networks and use them.
    Downloads: 1 This Week
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  • 8
    Imlementation of SOM (Self organizing networks) a neural network for IBM Cell Broadband Engine Architecture.
    Downloads: 0 This Week
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