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Name: Vishal Belsare
Type: User
Name: Vishal Belsare
Type: User
Additional kernels that can be used with scikit-learn's Gaussian Process module
Demonstration of a Gaussian Process regression on FX Forwards using Stan
Geometric programming for engineers
Genetic Programming in Python, with a scikit-learn inspired API
Useful subsystem models
Gaussian process multi-task learning
Implementation of Gaussian Process Panel Modeling in R
Pessimistic portfolio with sparse group lasso
General Pessimistic Risk Measure
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Common build environment used by gpuCI for building libgdf/pygdf
Gaussian processes framework in python
Gaussian Process Optimization using GPy
GraalVM: Run Programs Faster Anywhere :rocket:
A Python 3 implementation built on GraalVM
Graph-Constrained Estimation and Hypothesis Tests
Make huge neural nets fit in memory
The command line interface for Gradient - https://gradient.paperspace.com
A scikit-learn compatible library for graph kernels
The Knowledge Graph
GraMi is a novel framework for frequent subgraph mining in a single large graph, GraMi outperforms existing techniques by 2 orders of magnitudes. GraMi supports finding frequent subgraphs as well as frequent patterns, Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Also, GraMi supports user-defined structural and semantic constraints over the results, as well as approximate results. For more details, check our paper: Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, and Panos Kalnis. GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph. PVLDB, 7(7):517-528, 2014.
Your favorite Python graph libraries, scalable and interoperable. Graph databases in memory, and familiar graph APIs for cloud databases.
Implementation of the Cypher language for searching NetworkX graphs
This repository contains efficient implementation of Granger causality and its extensions. Please see the wiki page for instructions.
GRAph Parallel Environment
Joint work for regression methods on graph
Graph is a semantic database that is used to create data-driven applications.
A simple framework to perform ADMM on graphs of low-degree.
Flexible command line tool to create graphs from CSV data
Improved version of Stoer-Wagner algorithm for Graph mincut; Graph k-Cut, Graph k-connected component decomposition
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.