Pure Numpy Implementation of the Coherent Point Drift Algorithm
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Updated
Aug 8, 2023 - Python
Pure Numpy Implementation of the Coherent Point Drift Algorithm
Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…
Source files for "Fun Q: A Functional Introduction to Machine Learning in Q"
Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
Python implementation of EM algorithm for GMM. And visualization for 2D case.
By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.
Bayesian Methods for Machine Learning
Explaining textual analysis tools in Python. Including Preprocessing, Skip Gram (word2vec), and Topic Modelling.
A Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods.
Code for the algorithms in the paper: Vaibhav B Sinha, Sukrut Rao, Vineeth N Balasubramanian. Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment Classification. KDD WISDOM 2018
Code for GFlowNet-EM, a novel algorithm for fitting latent variable models with compositional latents and an intractable true posterior.
[MICCAI 2022 Best Paper Finalist] Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi Supervised Segmentation
Notebooks explaining the intuition behind the Expectation Maximisation algorithm
Animated Visualizations of Popular Machine Learning Algorithms
Python library to implement advanced trading strategies using machine learning and perform backtesting.
Code and data for the KDD2020 paper "Learning Opinion Dynamics From Social Traces"
A simple but generic implementation of Expectation Maximization algorithms to fit mixture models.
CLIP-seq Analysis of Multi-mapped reads
C++ library handling Gaussian Mixure Models
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