A library of scalable Bayesian generalised linear models with fancy features
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Updated
Sep 7, 2017 - Python
A library of scalable Bayesian generalised linear models with fancy features
Tensorflow 2.0 implementation of fourier feature mapping networks.
Image classification using SVM, KNN, Bayes, Adaboost, Random Forest and CNN.Extracting features and reducting feature dimension using T-SNE, PCA, LDA.
SCFGP: Sparsely Correlated Fourier Features Based Gaussian Process
Time series regression modeling on a dataset of supermarket sales across years, with the Darts library in Python.
Incremental Sparse Spectrum Gaussian Process Regression
An implementation of Fourier feature mapping method using TensorFlow 2.3
A Modern Spin on Ptolemy's Geocentric Universe Model
Neural Fields for Sea Surface Height Interpolation.
Forecasting Store Sales for Improved Decision-Making Using Machine Learning for Time Series Data
Sparse spectrum Gaussian process regression
Implementation of two phase field approaches for the surface reconstruction problem. One based of the Modica-Mortola theorem and the other based on Ambrosio-Tortorelli | Master Thesis
Unofficial pytorch implementation of the paper "Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding", NeurIPS 2021.
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