vishalbelsare Goto Github PK
Name: Vishal Belsare
Type: User
Vishal Belsare's Projects
gp_extras
Additional kernels that can be used with scikit-learn's Gaussian Process module
gp_regression
Demonstration of a Gaussian Process regression on FX Forwards using Stan
gpkit
Geometric programming for engineers
gplearn
Genetic Programming in Python, with a scikit-learn inspired API
gplibrary
Useful subsystem models
gpmtl
Gaussian process multi-task learning
gppm
Implementation of Gaussian Process Panel Modeling in R
gpqr
Pessimistic portfolio with sparse group lasso
gprm
General Pessimistic Risk Measure
gpsig
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
gpuci-build-environment
Common build environment used by gpuCI for building libgdf/pygdf
gpy
Gaussian processes framework in python
gpyopt
Gaussian Process Optimization using GPy
graal
GraalVM: Run Programs Faster Anywhere :rocket:
graalpython
A Python 3 implementation built on GraalVM
grace
Graph-Constrained Estimation and Hypothesis Tests
gradient-checkpointing
Make huge neural nets fit in memory
gradient-cli
The command line interface for Gradient - https://gradient.paperspace.com
grakel
A scikit-learn compatible library for graph kernels
grakn
The Knowledge Graph
grami
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.
grand
Your favorite Python graph libraries, scalable and interoperable. Graph databases in memory, and familiar graph APIs for cloud databases.
grand-cypher
Implementation of the Cypher language for searching NetworkX graphs
granger-causality
This repository contains efficient implementation of Granger causality and its extensions. Please see the wiki page for instructions.
grape
GRAph Parallel Environment
graph
Joint work for regression methods on graph
graph-1
Graph is a semantic database that is used to create data-driven applications.
graph-admm
A simple framework to perform ADMM on graphs of low-degree.
graph-cli
Flexible command line tool to create graphs from CSV data
graph-cut
Improved version of Stoer-Wagner algorithm for Graph mincut; Graph k-Cut, Graph k-connected component decomposition
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