A library of scalable Bayesian generalised linear models with fancy features

Image classification using SVM, KNN, Bayes, Adaboost, Random Forest and CNN.Extracting features and reducting feature dimension using T-SNE, PCA, LDA.

Unofficial pytorch implementation of the paper "Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding", NeurIPS 2021.

AG-MAE: Anatomically Guided Spatio-Temporal Masked Auto-Encoder for Online Hand Gesture Recognition

Incremental Sparse Spectrum Gaussian Process Regression

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

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.

Forecasting Store Sales for Improved Decision-Making Using Machine Learning for Time Series Data

Neural Fields for Sea Surface Height Interpolation.

Tensorflow 2.0 implementation of fourier feature mapping networks.

A Modern Spin on Ptolemy's Geocentric Universe Model

An implementation of Fourier feature mapping method using TensorFlow 2.3

Image regression experiments with random fourier feature mapping using PyTorch

Visualizing function fittings

This project implements coordinate-based neural networks from scratch in NumPy to reconstruct low- and high-resolution images by mapping 2D pixel coordinates to RGB values. It explores input encoding strategies—none, basic, and Gaussian Fourier features—to evaluate their impact on image quality and detail. Tech: Python (numpy, cv2, os)

O E-COMPRESS é um sistema híbrido de compressão de arquivos que combina redes neurais supervisionadas com técnicas tradicionais como gzip e validação de integridade por CRC e ECC. Ele segmenta arquivos em blocos independentes, codifica cada um com Fourier Features e redes MLP, e reconstrói com alta fidelidade.