Experiments for https://arxiv.org/abs/1912.02527
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Updated
Dec 9, 2019 - Go
Experiments for https://arxiv.org/abs/1912.02527
Statistical Machine Learning Project on Variational Inference
Implementation of Sparse Information Filter for Fast Gaussian Process Regression Kania et al. (2021) (https://link.springer.com/chapter/10.1007/978-3-030-86523-8_32)
We present a probabilistic model for neural spike counts that can capture arbitrary single neuron and joint statistics with their modulation by external covariates.
VSPsnap is a collection of R and Python code for Gaussian Process regression in a kriging-like setting (i.e. two features (X,Y) and a target (Z)) - with a focus on SARS-CoV2 data (genomic/IR/FR).
Investigative project for my CST Part III Probabilistic Machine Learning (LE49) module
Implementation of Kronecker inference for Gaussian Processes
A package for fitting (curve restricted) smoothing splines of degrees 2p-1 using the Gaussian process view with a rank structured kernel matrix.
Sampling from stochastic processes in Python.
This repository presents our research on optimizing crutch designs using Gaussian Processes (GPs) and Bayesian Optimization (BO). We introduce a novel loss function that blends subjective (pain, instability, effort) and objective measures, leading to a personalized, more efficient, and comfortable crutch design.
Companion R code for the book Bayesian Optimization with Application to Computer Experiments
Stochastic Process Library for Python
GPoFM: Gaussian Process Training with Optimized Feature Maps for Shift-Invariant Kernels
Projet ENSAE : Optimisation bayésienne d'algorithmes de Machine Learning
c_ANOVA - POD - Krigging method for Uncertatnty Quantification
squidward is a package for gaussian process modeling
Gauss process integrated with forward automatic differentiation
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