Search Results for "linux-libre kernel"
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Showing 10 open source projects for "linux-libre kernel"
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of... -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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Netgen Mesh Generator
NETGEN is an automatic 3d tetrahedral mesh generator
NETGEN is an automatic 3d tetrahedral mesh generator. It accepts input from constructive solid geometry (CSG) or boundary representation (BRep) from STL file format. The connection to a geometry kernel allows the handling of IGES and STEP files. NETGEN contains modules for mesh optimization and hierarchical mesh refinement. Netgen is open source based on the LGPL license. It is available for Unix/Linux and Windows. -
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libirt
The new site is at http://psychometricon.net/libirt/
The new site is at http://psychometricon.net/libirt/ Library of functions to estimate the items and abilities from the responses of subjects to a questionnaire. The IRT models supported are the logistic model, the multivariate logistic model, the graded model and smoothing by penalization and kernel. -
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riboshape
Predicting ribosome footprint profile shapes from transcript sequences
Riboshape is a suite of algorithms to predict ribosome footprint profile shapes from transcript sequences. It applies kernel smoothing to codon sequences to build predictive features, and uses these features to builds a sparse regression model to predict the ribosome footprint profile shapes. Reference: Liu, T.-Y. and Song, Y.S. Prediction of ribosome footprint profile shapes from transcript sequences. Proceedings of ISMB 2016, Bioinformatics, Vol. 32 No. 12 (2016) i183-i191. -
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ViennaCL
Linear algebra and solver library using CUDA, OpenCL, and OpenMP
ViennaCL provides high level C++ interfaces for linear algebra routines on CPUs and GPUs using CUDA, OpenCL, and OpenMP. The focus is on generic implementations of iterative solvers often used for large linear systems and simple integration into existing projects. -
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Adaptive Gaussian Filtering
Machine learning with Gaussian kernels.
Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation... -
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ViennaMath
A symbolic math kernel in C++
ViennaMath provides a symbolic math kernel which can be used either for compile-time processing, or for run-time evaluation. Unlike other symbolic math implementations, ViennaMath aims at providing a fast math layer for use with numerical methods such as the finite element method (cf. ViennaFEM) -
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ViennaFEM
A finite element solver using state-of-the-art programming techniques.
ViennaFEM is a library-centric implementation of the finite element method in C++. It features a symbolic math kernel, which manipulates the strong or weak form of the problem and automatically derives the discrete form. ViennaFEM is built on top of the following libraries: ViennaMath provides the symbolic math kernel, ViennaGrid (with ViennaData) allows for a generic grid handling and quantity storage, while ViennaCL provides the linear solvers and GPU acceleration. -
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The Optimized Sparse Kernel Interface (OSKI) Library provides automatically tuned sparse matrix kernels, for use by solver libraries and applications. OSKI is part of the BeBOP project on performance tuning and analysis at U.C. Berkeley. (Go Bears!)
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