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2048-deep-reinforcement-learning

Trained A Convolutional Neural Network To Play 2048 using Deep-Reinforcement Learning

a-divide-and-conquer-strategy-for-high-dimensional-bayesian-factor-models

We propose a distributed computing framework, based on a divide and conquer strategy and hierarchical modeling, to accelerate posterior inference for high-dimensional factor models. Our approach distributes the task of high-dimensional covariance matrix estimation to multiple cores, solves each subproblem separately via a latent factor model, and then combines these estimates to produce a globale estimate of the covariance matrix. The MATLAB code is available for public use. The paper is available at https://arxiv.org/pdf/1612.02875.pdf.

acados

Fast and embedded solvers for nonlinear optimal control

acatama

AcATaMa is a Qgis plugin for Accuracy Assessment of Thematic Maps

adaptive-thresholding

Implement the JASA paper "Adaptive Thresholding for Sparse Covariance Matrix Estimation"

admm

This Matlab package solves the sparse and low-rank covariance matrix estimation.

advanced_techniques_for_curvefitting

Advanced Technique for Curve Fitting

amsa

Datasets for the 6th Edition of the book Applied Multivariate Statistical Analysis by Richard Johnson and Dean Wichern

ann_lvq

Learning Vector Quantization - Artificial Neural Network

anyprog

A C++ scientific library for mathematical programming,data fitting and solving nonlinear equations

awesome-multi-objective-optimization

A curated list of awesome multi-objective optimization research resources.

blavaan

An R package for Bayesian structural equation modeling

bmobench

Black-Box Multi-Objective Optimization Benchmarking Platform

book

burgers-equation

solves the steady (time-independent) viscous Burgers equation using a finite difference discretization of the conservative form of the equation, and then applying Newton's method to solve the resulting nonlinear system

c-rsce

This is the realdata for our paper titled as "Robust Sparse Covariance Matrix Estimation for Compositional Data"

ceres-solver

A Nonlinear Least Squares Minimizer

ceres-solver-1

A large scale non-linear optimization library

chance-optimization

Semidefinite Programming for Nonlinear Chance Optimization

cmdstan

CmdStan, the command line interface to Stan

cmetest

Covariance Matrix Robust Estimation and Random Matrix Theory Filtering

color-image-segmentation-using-granular-computing-technique

Image segmentation is the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze [1]. Image segmentation is typically used to locate objects and boundaries in images. Segmentation should stop when the Region of Interest (ROI) in an application have been isolated. An example is, if the image application aims to recognize the iris in an eye image, then the iris in the eye image is the required ROI. Segmentation extracts the Regions of Interest (ROI) from the image to form a similar region by classifying pixels on some basis to group them into a region of similarity. project implemented as windows standalone application

complexnetworksbook.github.io

Resources for the book "Complex Networks: A Networking and Signal Processing Perspective"

complexnonlinearities

Complex Nonlinearities for Audio Signal Processing

confusion-matrix

Confusion matrix for supervised classification

cov2u

Monte Carlo estimation of parameter uncertainty from parameter covariance matrix for AVHRR satellite harmonisation data

covar-cryo

Estimation of the covariance matrix for analyzing heterogeneity in cryo_EM data

covariance-matrix-estimation-and-knn-classifier-for-facial-expression-recognition

Code for extracting multi-scale LBP features and geometric features of human face point cloud, then estimating covariance matrix from the fusion of these feature before classification.

covarianceestimation-massivemimo

This is the code that generates the figures in the paper titled "Covariance Matrix Estimation for Massive MIMO" authored by Karthik Upadhya and Sergiy A. Vorobyov and published in IEEE Signal Processing Letters, vol. 25, no. 4, pp. 546-550, April 2018.

covfactormodel

Covariance Matrix Estimation via Factor Models