Java·Applied·Geodesy·3D - Least-Squares Adjustment Software for Geodetic Sciences
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Updated
Jun 2, 2024 - Java
Java·Applied·Geodesy·3D - Least-Squares Adjustment Software for Geodetic Sciences
All Assignments of the course, Statistical Methods in AI at IIITH, Monsoon 2024
Probabilistic sequence generation of sketch drawings based upon Google Brain's "A Neural Representation of Sketch Drawings"
This visualization toolkit demonstrates the convergence of a Gaussian Mixture Model (GMM) in 3D and 2D spaces, featuring interactive elements, optimal centroid initialization via K-means++, and covariance matrix regularization for enhanced numerical stability.
Gaussian Mixture Regression
Traditional Machine Learning Models for Large-Scale Datasets in PyTorch.
The Anonymous Synthesizer for Health Data
Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)
🎓 Advanced Multivariate Statistics in Python [UniMi • AY 2022/2023]
Machine learning model implementations from scratch in Python
This repository implements K-Means clustering on the Old-Faithful dataset, with visualization of clustering iterations and distortion. It also applies K-Means for image compression/segmentation and utilizes the EM algorithm with a Gaussian Mixture Model for classification.
Open source code for paper "Robust Group Anomaly Detection for Quasi-Periodic Network Time Series"
ENCM 509 - Fundamentals of Biometric Systems Design - Winter 2024
R package for maximal likelihood estimation of multivariate normal mixture models
Code and data associated with the paper "Superiority of quadratic over conventional neural networks for classification of Gaussian mixture data."
Bundle Adjustment for Close-Range Photogrammetry
Matlab functions to plot 2D and 3D maps from nanoindentation tests.
Coding solutions to various image processing problems integrating statistical algorithm known as Expectation-Maximization (EM), and clustering algorithm known as Gaussian Mixture Model (GMM).
Easy to read Pytorch implementation of same-family gaussian mixture models. Features separable parameter optimization and singularity mitigation
DD2434 - Machine Learning Advanced projects at KTH.
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