Showing 767 open source projects for "linear"

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  • 1
    LinearOperators.jl

    LinearOperators.jl

    Linear Operators for Julia

    Operators behave like matrices (with some exceptions - see below) but are defined by their effect when applied to a vector. They can be transposed, conjugated, or combined with other operators cheaply. The costly operation is deferred until multiplied with a vector.
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  • 2
    NLPModels.jl

    NLPModels.jl

    Data Structures for Optimization Models

    This package provides general guidelines to represent non-linear programming (NLP) problems in Julia and a standardized API to evaluate the functions and their derivatives. The main objective is to be able to rely on that API when designing optimization solvers in Julia.
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  • 3
    Gridap.jl

    Gridap.jl

    Grid-based approximation of partial differential equations in Julia

    Gridap provides a set of tools for the grid-based approximation of partial differential equations (PDEs) written in the Julia programming language. The library currently supports linear and nonlinear PDE systems for scalar and vector fields, single and multi-field problems, conforming and nonconforming finite element (FE) discretizations, on structured and unstructured meshes of simplices and n-cubes. It also provides methods for time integration. Gridap is extensible and modular. One can...
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  • 4
    Rouille

    Rouille

    Web framework in Rust

    Rouille is a micro-web-framework library. It creates a listening socket and parses incoming HTTP requests from clients, then gives you the hand to process the request. Rouille was designed to be intuitive to use if you know Rust. Contrary to express-like frameworks, it doesn't employ middlewares. Instead, everything is handled in a linear way. Concepts closely related to websites (like cookies, CGI, form input, etc.) are directly supported by rouille. More general concepts (like database...
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  • 5
    Recurrent Interface Network (RIN)

    Recurrent Interface Network (RIN)

    Implementation of Recurrent Interface Network (RIN)

    ... for larger images. The big surprise is that the generations can reach this level of fidelity. Will need to verify this on my own machine. Additionally, we will try adding an extra linear attention on the main branch as well as self-conditioning in the pixel space. The insight of being able to self-condition on any hidden state of the network as well as the newly proposed sigmoid noise schedule are the two main findings.
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  • 6
    spaGO

    spaGO

    Self-contained Machine Learning and Natural Language Processing lib

    ... to ensure that additional dependencies are downloaded only when specific features (e.g. persistent embeddings) are used. A good place to start is by looking at the implementation of built-in neural models, such as the LSTM. Except for a few linear algebra operations written in assembly for optimal performance (a bit of copying from Gonum), it's straightforward Go code, so you don't have to worry.
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  • 7
    julep

    julep

    A new DSL and server for AI agents and multi-step tasks

    Julep is a platform for creating AI agents that remember past interactions and can perform complex tasks. It offers long-term memory and manages multi-step processes. Julep enables the creation of multi-step tasks incorporating decision-making, loops, parallel processing, and integration with numerous external tools and APIs. While many AI applications are limited to simple, linear chains of prompts and API calls with minimal branching, Julep is built to handle more complex scenarios.
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  • 8
    elasticsearch-learning-to-rank

    elasticsearch-learning-to-rank

    Plugin to integrate Learning to Rank

    The Elasticsearch Learning to Rank plugin uses machine learning to improve search relevance ranking. It's powering search at places like Wikimedia Foundation and Snagajob.
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  • 9
    The Operator Splitting QP Solver

    The Operator Splitting QP Solver

    The Operator Splitting QP Solver

    OSQP uses a specialized ADMM-based first-order method with custom sparse linear algebra routines that exploit structure in problem data. The algorithm is absolutely division-free after the setup and it requires no assumptions on problem data (the problem only needs to be convex). It just works. OSQP has an easy interface to generate customized embeddable C code with no memory manager required. OSQP supports many interfaces including C/C++, Fortran, Matlab, Python, R, Julia, Rust.
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  • 10
    NeuroMatch Academy (NMA)

    NeuroMatch Academy (NMA)

    NMA Computational Neuroscience course

    ... brush up on your knowledge. The pre-reqs refresher days are asynchronous, so you can go through the material on your own time. You will learn how to code in Python from scratch using a simple neural model, the leaky integrate-and-fire model, as a motivation. Then, you will cover linear algebra, calculus and probability & statistics. The topics covered on these days were carefully chosen based on what you need for the comp neuro course.
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  • 11
    Djot

    Djot

    A light markup language

    Djot is a light markup syntax. It derives most of its features from commonmark, but it fixes a few things that make commonmark's syntax complex and difficult to parse efficiently. It is also much fuller-featured than commonmark, with support for definition lists, footnotes, tables, several new kinds of inline formatting (insert, delete, highlight, superscript, subscript), math, smart punctuation, attributes that can be applied to any element, and generic containers for block-level,...
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  • 12
    Argo Events

    Argo Events

    Event-driven Automation Framework for Kubernetes

    Argo Events is an event-driven workflow automation framework for Kubernetes. It allows you to trigger 10 different actions (such as the creation of Kubernetes objects, invoking workflows or serverless workloads) on over 20 different events (such as webhook, S3 drop, cron schedule, messaging queues - e.g. Kafka, GCP PubSub, SNS, SQS).
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  • 13
    JUDI.jl

    JUDI.jl

    Julia Devito inversion

    ... operators can also be used as layers in (convolutional) neural networks to implement physics-augmented deep learning algorithms thanks to its implementation of ChainRules's rrule for the linear operators representing the discre wave equation.
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  • 14
    MultilayerGraphs.jl

    MultilayerGraphs.jl

    Julia package for the creation and analysis of multilayer graphs

    MultilayerGraphs.jl is a Julia package for the creation, manipulation and analysis of the structure, dynamics and functions of multilayer graphs. A multilayer graph is a graph consisting of multiple standard subgraphs called layers which can be interconnected through bipartite graphs called interlayers composed of the vertex sets of two different layers and the edges between them. The vertices in each layer represent a single set of nodes, although not all nodes have to be represented in...
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  • 15
    DiffOpt.jl

    DiffOpt.jl

    Differentiating convex optimization programs w.r.t. program parameters

    DiffOpt.jl is a package for differentiating convex optimization programs (JuMP.jl or MathOptInterface.jl models) with respect to program parameters. Note that this package does not contain any solver. This package has two major backends, available via the reverse_differentiate! and forward_differentiate! methods, to differentiate models (quadratic or conic) with optimal solutions. Differentiable optimization is a promising field of convex optimization and has many potential applications in...
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  • 16
    Gaius.jl

    Gaius.jl

    Divide and Conquer Linear Algebra

    Gaius.jl is a multi-threaded BLAS-like library using a divide-and-conquer strategy to parallelism, and built on top of the fantastic LoopVectorization.jl. Gaius spawns threads using Julia's depth-first parallel task runtime and so Gaius's routines may be fearlessly nested inside multi-threaded Julia programs. Gaius is not stable or well-tested. Only use it if you're adventurous.
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  • 17
    Polyhedra

    Polyhedra

    Polyhedral Computation Interface

    Polyhedra provides an unified interface for Polyhedral Computation Libraries such as CDDLib.jl. This manipulation notably includes the transformation from (resp. to) an inequality representation of a polyhedron to (resp. from) its generator representation (convex hull of points + conic hull of rays) and projection/elimination of a variable with e.g. Fourier-Motzkin.
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  • 18
    ColorSchemes.jl

    ColorSchemes.jl

    colorschemes, colormaps, gradients, and palettes

    Color schemes, colormaps, gradients, and palettes. Choose ColorSchemes with care. Refer to Peter Kovesi's PerceptualColourMaps package, or to Fabio Crameri's Scientific Colour Maps for more information. If you want to make more advanced ColorSchemes, use linear-segment dictionaries or indexed lists, and use functions to generate color values, see the make_colorscheme() function in the ColorSchemeTools.jl package.
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  • 19
    BlockArrays.jl

    BlockArrays.jl

    BlockArrays for Julia

    ... BlockArray stores each block contiguously while the type PseudoBlockArray stores the full matrix contiguously. This means that BlockArray supports fast noncopying extraction and insertion of blocks while PseudoBlockArray supports fast access to the full matrix to use in for example a linear solver.
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  • 20
    Surrogates.jl

    Surrogates.jl

    Surrogate modeling and optimization for scientific machine learning

    A surrogate model is an approximation method that mimics the behavior of a computationally expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function f(p), but each calculation of f is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model g which approximates f by training on previous data collected from evaluations...
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  • 21
    ReverseDiff

    ReverseDiff

    Reverse Mode Automatic Differentiation for Julia

    ReverseDiff is a fast and compile-able tape-based reverse mode automatic differentiation (AD) that implements methods to take gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really). While performance can vary depending on the functions you evaluate, the algorithms implemented by ReverseDiff generally outperform non-AD algorithms in both speed and accuracy.
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  • 22
    CellTypist

    CellTypist

    A tool for semi-automatic cell type classification, harmonization

    CellTypist is an automated tool for cell type classification, harmonization, and integration. Classification, transfer cell type labels from the reference to query dataset. Harmonization, match and harmonize cell types defined by independent datasets. integration, integrate cell and cell types with supervision from harmonization. CellTypist recapitulates cell type structure and biology of independent datasets. Regularised linear models with Stochastic Gradient Descent provide a fast...
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  • 23
    Alpa

    Alpa

    Training and serving large-scale neural networks

    Alpa is a system for training and serving large-scale neural networks. Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code.
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  • 24
    MLT Multimedia Framework

    MLT Multimedia Framework

    MLT Multimedia Framework

    Author, manage, and run multitrack audio/video compositions. The engine of a non-linear video editor that can be used in all sorts of apps, not just desktop video editors. MLT is an open source multimedia framework, designed and developed for television broadcasting. It provides a toolkit for broadcasters, video editors, media players, transcoders, web streamers and many more types of applications. The functionality of the system is provided via an assortment of ready-to-use tools, XML...
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  • 25
    Hono

    Hono

    Web framework built on Web Standards

    Hono is a small, simple, and ultrafast web framework built on Web Standards. It works on any JavaScript runtime: Cloudflare Workers, Fastly Compute, Deno, Bun, Vercel, AWS Lambda, Lambda@Edge, and Node.js. The router RegExpRouter is really fast. The hono/tiny preset is under 14kB. Using only Web Standard APIs. Works on Cloudflare, Fastly, Deno, Bun, AWS, or Node.js. The same code runs on all platforms. Hono has built-in middleware, custom middleware, third-party middleware, and helpers....
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