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Showing 14 open source projects for "network"
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AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
Beyond its much publicized success in attaining superhuman level at games such as Chess and Go, DeepMind's AlphaZero algorithm illustrates a more general methodology of combining learning and search to explore large combinatorial spaces effectively. We believe that this methodology can have exciting applications in many different research areas. Because AlphaZero is resource-hungry, successful open-source implementations (such as Leela Zero) are written in low-level languages (such as C++)... -
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ITensors.jl
A Julia library for efficient tensor computations and tensor network
ITensors.jl is a high-performance Julia library for tensor network calculations, primarily used in quantum physics and computational science. It enables efficient manipulation of large, structured tensors with named indices and provides an intuitive interface for implementing algorithms like DMRG (Density Matrix Renormalization Group), TEBD (Time-Evolving Block Decimation), and more. ITensors.jl leverages Julia’s multiple dispatch and performance features to simplify the development of scalable and complex simulations involving quantum many-body systems. -
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Gen.jl
A general-purpose probabilistic programming system
...But it also lets users migrate parts of their model or inference algorithm to specialized modeling languages for which it can generate especially fast code. Users can also hand-code parts of their models that demand better performance. Neural network inference is fast, but can be inaccurate on out-of-distribution data, and requires expensive training. -
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NNlib.jl
Neural Network primitives with multiple backends
This package provides a library of functions useful for neural networks, such as softmax, sigmoid, batched multiplication, convolutions and pooling. Many of these are used by Flux.jl, which loads this package, but they may be used independently. -
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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
...It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function spaces. The kernel can be trained on different geometry, which is learned from a graph. Fourier neural operator learns a neural operator with Dirichlet kernel to form a Fourier transformation. ... -
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OpenAPI
OpenAPI helper and code generator for Julia
...The goal of OpenAPI is to define a standard, language-agnostic interface to REST APIs which allows both humans and computers to discover and understand the capabilities of the service without access to source code, documentation, or through network traffic inspection. When properly defined via OpenAPI, a consumer can understand and interact with the remote service with a minimal amount of implementation logic. Similar to what interfaces have done for lower-level programming, OpenAPI removes the guesswork in calling the service. Check out OpenAPI-Spec for additional information about the OpenAPI project, including additional libraries with support for other languages and more. -
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GraphNeuralNetworks.jl
Graph Neural Networks in Julia
GraphNeuralNetworks.jl is a graph neural network library written in Julia and based on the deep learning framework Flux.jl. -
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Catalyst.jl
Chemical reaction network and systems biology interface
Catalyst.jl is a symbolic modeling package for analysis and high-performance simulation of chemical reaction networks. Catalyst defines symbolic ReactionSystems, which can be created programmatically or easily specified using Catalyst's domain-specific language (DSL). Leveraging ModelingToolkit and Symbolics.jl, Catalyst enables large-scale simulations through auto-vectorization and parallelism. Symbolic ReactionSystems can be used to generate ModelingToolkit-based models, allowing the easy... -
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Graphs.jl
An optimized graphs package for the Julia programming language
The goal of Graphs.jl is to offer a performant platform for network and graph analysis in Julia, following the example of libraries such as NetworkX in Python. Offers a set of simple, concrete graph implementations – SimpleGraph (for undirected graphs) and SimpleDiGraph (for directed graphs), an API for the development of more sophisticated graph implementations under the AbstractGraph type, and a large collection of graph algorithms with the same requirements as this API. -
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CausalityTools.jl
Algorithms for detecting associations, dynamical influences
CausalityTools.jl is a package for quantifying associations and dynamical coupling between datasets, independence testing, and causal inference. Association measures from conventional statistics, information theory, and dynamical systems theory, for example, distance correlation, mutual information, transfer entropy, convergent cross mapping and a lot more. A dedicated API for independence testing, which comes with automatic compatibility with every measure-estimator combination you can... -
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Kinetic.jl
Universal modeling and simulation of fluid mechanics upon ML
...It aims to furnish efficient modeling and simulation methodologies for fluid dynamics, augmented by the power of machine learning. Based on differentiable programming, mechanical and neural network models are fused and solved in a unified framework. Simultaneous 1-3 dimensional numerical simulations can be performed on CPUs and GPUs. -
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StackExchangeCodes
Codes related to answers on StackExchange Network
Codes related to answers by Royi Avital on StackExchange Network. -
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LightGraphs
An optimized graphs package for the Julia programming language
LightGraphs offers both (a) a set of simple, concrete graph implementations -- Graph (for undirected graphs) and DiGraph (for directed graphs), and (b) an API for the development of more sophisticated graph implementations under the AbstractGraph type. The project goal is to mirror the functionality of robust network and graph analysis libraries such as NetworkX while being simpler to use and more efficient than existing Julian graph libraries such as Graphs.jl. It is an explicit design decision that any data not required for graph manipulation (attributes and other information, for example) is expected to be stored outside of the graph structure itself. ... -
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Mocha.jl
Deep Learning framework for Julia
...Reworking Mocha with new technologies requires some non-trivial efforts, and new exciting solutions already exist nowadays, it is a good time for the retirement of Mocha.jl. Mocha has a clean architecture with isolated components like network layers, activation functions, solvers, regularizers, initializers, etc.
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