MLxtend
MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack - Published in JOSS (2018)
geocmeans
geocmeans: An R package for spatial fuzzy c-means - Published in JOSS (2023)
Persistable
Persistable: persistent and stable clustering - Published in JOSS (2023)
ClassiPyGRB
ClassiPyGRB: Machine Learning-Based Classification and Visualization of Gamma Ray Bursts using t-SNE - Published in JOSS (2024)
t-elf
Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the estimation of latent factors - rank) for accurate data modeling. Our software suite encompasses cutting-edge data pre-processing and post-processing modules.
susi
SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
hyperspectral-regression
Code examples for the book chapter "Supervised, Semi-Supervised and Unsupervised Learning for Hyperspectral Regression".
anomalib
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
boxsers
Python package that provides a full range of functionality to process and analyze vibrational spectra (Raman, SERS, FTIR, etc.).
xcsf
XCSF learning classifier system: rule-based online evolutionary machine learning
goneat
The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to evolve and train Artificial Neural Networks without error back propagation
metacluster
MetaCluster: An Open-Source Python Library for Metaheuristic-based Clustering Problems
https://github.com/sintel-dev/orion
Unsupervised time series anomaly detection library
lgn-autoencoder
Lorentz group equivariant autoencoders based on Lorentz Group Network
minisom
:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
https://github.com/yzhao062/pyod
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
SGCP
SGCP: a spectral self-learning method for clustering genes in co-expression networks
tyc-dataset
Official and maintained implementation of the dataset paper "The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures" [ICCVW 2023].
com.linkedin.isolation-forest
A distributed Spark/Scala implementation of the isolation forest algorithm for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.
unsupervised_analysis
A general purpose Snakemake workflow and MrBiomics module to perform unsupervised analyses (dimensionality reduction & cluster analysis) and visualizations of high-dimensional data.
https://github.com/ermshaua/claspy
ClaSPy: A Python package for time series segmentation.
https://github.com/cbg-ethz/jnotype
Probabilistic modeling of high-dimensional binary data in JAX
gap-stat
Dynamically get the suggested clusters in the data for unsupervised learning.
rsetse
This package is used to calculate the Strain Elevation Tension Spring embedding (SETSe) for networks in R
https://github.com/christophreich1996/smurf
PyTorch port (inference only) of the paper "SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping" [CVPR 2021].
https://github.com/chris-santiago/decomposition
Simple ISOMAP and PCA decomposition algorithms
https://github.com/chris-santiago/gmm
Gaussian Mixture Model with low rank approximation
tsdae
Tranformer-based Denoising AutoEncoder for Sentence Transformers Unsupervised pre-training.
https://github.com/biodataanalysisgroup/kmeranalyzer
An alignment-free method capable of processing and counting k-mers in a reasonable time, while evaluating multiple values of the k parameter concurrently.
https://github.com/chenliu-1996/cuts
[MICCAI 2024] CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
https://github.com/chris-santiago/bookmarks-topics
Using unsupervised learning and language modeling to cluster and reorganize web bookmarks.
https://github.com/ashrithsagar/cp217-ml4cps-2024
CP 217 Machine Learning for Cyber-Physical Systems, IISc
https://github.com/barabasi-lab/ai-bind
Interpretable AI pipeline improving binding predictions for novel protein targets and ligands
clusteranalysisbasedfastingpostprandialglucoseinsulin
Code of the paper "Cluster Analysis Based on Fasting and Postprandial Plasma Glucose and Insulin Concentrations"
approaching-an-unknown-communication-system
Code/supplement for the paper "Approaching an unknown communication system by latent space exploration and causal inference"
imesc
This app is intended to dynamically integrate machine learning techniques to explore multivariate data sets.
indonesian-sentence-embeddings
Embedding Representation for Indonesian Sentences!
ml-oneday-course
This is a one-day machine learning introductory course for beginners
https://github.com/aiot-mlsys-lab/arch2vec
[NeurIPS 2020] "Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?" by Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, Mi Zhang
https://github.com/awslabs/unsupervised-qa
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering
r-python
Denetimsiz Makine Öğrenmesi Algoritmaları: R ve Python Uygulamaları Kitabındaki R ve Python kodları
https://github.com/danymukesha/pca-pwa
simplified manner for insights and decision-making by visualizing complex relationships with PCA web application
https://github.com/cern-it-innovation/latent-ad-qml
Unsupervised anomaly detection in the latent space of high energy physics events with quantum machine learning.
https://github.com/ammarlodhi255/ml_spelled_out
Collection of notebooks and python code of various machine learning algorithms from scratch.
https://github.com/agopal42/permakey
Code repository complementing the ICLR 2021 paper "Unsupervised Object Keypoint Learning using Local Spatial Predictability" (https://arxiv.org/abs/2011.12930)
https://github.com/aiot-mlsys-lab/cate
[ICML 2021 Oral] "CATE: Computation-aware Neural Architecture Encoding with Transformers" by Shen Yan, Kaiqiang Song, Fei Liu, Mi Zhang