Efficient approximate Bayesian machine learning
-
Updated
Oct 26, 2024 - Python
Efficient approximate Bayesian machine learning
Advanced Image Enhancement and Data Recovery: Superresolution Techniques and Missing Data Handling
Bayesian inference using sparse gaussian processes from tinygp. Examples include 1D and 2D implementation.
[Pattern Recognition 2023] End-to-end Kernel Learning via Generative Random Fourier Features
PySVM : A NumPy implementation of SVM based on SMO algorithm. Numpy构建SVM分类、回归与单分类,支持缓存机制与随机傅里叶特征
The official implementation of Randomly Weighted Feature Network for Visual Relationship Detection Tasks (CLeaR@AAAI2022)
The official implementation of Randomly Weighted Feature Network for Visual Relationship Detection Tasks (CLeaR@AAAI2022)
Johnson-Lindenstrauss transform (JLT), random projections (RP), fast Johnson-Lindenstrauss transform (FJLT), and randomized Hadamard transform (RHT) in python 3.x
[AISTATS 2023] Error Estimation for Random Fourier Features
LINMA2472: Algorithms in Data Science
GRB triangulation via non-stationary time-series models
A time-delayed light curve simulation code for GRB location triangulation via random Fourier features.
Incremental Sparse Spectrum Gaussian Process Regression
Python implementation of the paper Random Fourier Features based SLAM (https://arxiv.org/pdf/2011.00594.pdf)
Add a description, image, and links to the random-fourier-features topic page so that developers can more easily learn about it.
To associate your repository with the random-fourier-features topic, visit your repo's landing page and select "manage topics."