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Machine Learning ⭐ 67

Programming Assignments and Lectures for Andrew Ng's "Machine Learning" Coursera course

One-Dimensional Statistical Parametric Mapping in Python

SPM (Statistical Parametric Mapping) - Development Version

analysis of brain imaging data sequences

β-VAE for intelligent control of a five band parametric EQ

🔀 Medical software for Processing multi-Parametric images Pipelines

The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.

Texturesynth ⭐ 31

Matlab implementation of Efros-Leung texture synthesis by non-parametric sampling.

Volumetrictruss ⭐ 27

Source code for the paper "Volumetric Michell Trusses for Parametric Design and Fabrication" published at SCF 2019.

Spm1dmatlab ⭐ 24

One-Dimensional Statistical Parametric Mapping in Matlab.

Parametric_modeling ⭐ 21

Parametric modelling functions that can be found in MATLAB now in python

Snpm Devel ⭐ 19

Statistical NonParametric Mapping, development version

Dyngenie3 ⭐ 18

Semi-parametric approach for the inference of gene regulatory networks from time series of expression data

Matlabauc ⭐ 15

Matlab code for the area under the receiver operating curve (AUC) and confidence intervals

Python package for data analysis for dipolar EPR spectroscopy

Parametricgp In Matlab ⭐ 11

Parametric Gaussian Process Regression for Big Data (Matlab Version)

Explicit Mpc ⭐ 10

Algorithms for implementation the Explicit MPC using the multi-parametric quadratic programming.

analysis of brain imaging data sequences

Perceptual_saliency_of_early_reflections ⭐ 10

Model for selecting perceptually relevant early reflections for parametric spatial sound rendering

Code for Ragav Venkatesan, Parag Shridhar Chandakkar, Baoxin Li "Simpler non-parametric methods provide as good or better results to multiple-instance learning." at the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015.

Physically-consistent GMM fitting approach proposed by Figueroa, N. and Billard, A. (2018) "A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning". In Proceedings of the 2nd Conference on Robot Learning (CoRL).

Matconvdag ⭐ 8

Convolutional Directed Acyclic Graph Network, built on top of vlfeat/matconvnet

Coursera_machine_learning ⭐ 7

About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this clas

Coursera Ml ⭐ 7

💡This repository contains all of the lecture exercises of Machine Learning course by Andrew Ng, Stanford University @ Coursera. All are implemented by myself and in MATLAB/Octave.

Lectures_on_neuroimaging ⭐ 6

Score Informed Source Separation ⭐ 6

Matlab code of the algorithm described in the paper "Score informed audio source separation using a parametric model of non-negative spectrogram" by R. Hennequin, B. David and R. Badeau

Dynamic PET simulator of tracers via emission projection for kinetic modeling and parametic image studies

Stochastic Bifurcation Analyser

Active Depth From Defocus ⭐ 5

Acticircadian ⭐ 5

Perform parametric and non-parametric analysis for extraction of circadian features from actigraphy data

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