cherakhan Goto Github PK

gaussian-ridges

Codes for dimension reduction using Gaussian Ridge Functions

generative-models

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

gp-mpc

MPC with Gaussian Process

gpmpc

gaussian process model predictive control in MATLAB

gpo-ifac2014

Particle filter-based Gaussian process optimisation for parameter inference

hado-sim

An Bayesian optimal experimental design framework to discriminate between active learning models in Cognitive Science

harmonic-oscillator-pinn

Code accompanying my blog post: So, what is a physics-informed neural network?

icardio

Code associated with the iCardio publication

intro-qc-triumf

Materials and notebooks for my first lecture series at TRIUMF, "An introduction to quantum computing and quantum annealing".

laplace_based_sequential_design_algorithms

latent_ode

Code for "Latent ODEs for Irregularly-Sampled Time Series" paper

learning-based-mpc

Learning-Based Model Predictive Control (LBMPC)

lwpls

Locally-Weighted Partial Least Squares (LWPLS)

machine-learning

Content for Udacity's Machine Learning curriculum

mars

Discovering novel cell types across heterogenous single-cell experiments

mattfa

A Matlab implementation of Thermodynamics-based Flux Analysis

mcmc

Implementation of Markov Chain Monte Carlo in Python from scratch

mcmcstat

MCMC toolbox for Matlab

mit-deep-learning

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

mnist_digit_recognition

MNIST hand-written digit recognition by fully-connected and convolutional neural networks - boiler plate code for easy reproduction and tutorial purpose.

mpc_gutbacteria

Implementation of the Model Predictive Control for the regulation of the intestinal bacterial overgrowth

mps

Myopic Posterior Sampling for Adaptive Bayesian Design of Experiments

multilevelot

MATLAB codes for multilevel optimal transport

networkinference

Code supporting paper titled 'Exploiting network topology for large-scale inference of nonlinear reaction models'.

nymp_2018

opti

OPTI Toolbox

optimal-design

Code to compute Optimal Experimental Design as in Balietti, Klein & Riedl (2020)

optimal-transport

Group project "Algorithms for large-scale optimal transport". Implement ADMMs and Sinkhorn's Algorithms.

optimization-python

General optimization (LP, MIP, QP etc.) using Python

particle-filter

Particle filters or Sequential Monte Carlo (SMC) methods are a set of genetic, Monte Carlo algorithms used to solve filtering problems arising in signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made, and random perturbations are present in the sensors as well as in the dynamical system. The objective is to compute the posterior distributions of the states of some Markov process, given some noisy and partial observations. Particle filters implement the prediction-updating transitions of the filtering equation directly by using a genetic type mutation-selection particle algorithm. The samples from the distribution are represented by a set of particles; each particle has a likelihood weight assigned to it that represents the probability of that particle being sampled from the probability density function.