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
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/agopal42/permakey
Code repository complementing the ICLR 2021 paper "Unsupervised Object Keypoint Learning using Local Spatial Predictability" (https://arxiv.org/abs/2011.12930)
approaching-an-unknown-communication-system
Code/supplement for the paper "Approaching an unknown communication system by latent space exploration and causal inference"
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.
indonesian-sentence-embeddings
Embedding Representation for Indonesian Sentences!
https://github.com/chris-santiago/bookmarks-topics
Using unsupervised learning and language modeling to cluster and reorganize web bookmarks.
imesc
This app is intended to dynamically integrate machine learning techniques to explore multivariate data sets.
https://github.com/awslabs/unsupervised-qa
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering
clusteranalysisbasedfastingpostprandialglucoseinsulin
Code of the paper "Cluster Analysis Based on Fasting and Postprandial Plasma Glucose and Insulin Concentrations"
https://github.com/barabasi-lab/ai-bind
Interpretable AI pipeline improving binding predictions for novel protein targets and ligands
r-python
Denetimsiz Makine Öğrenmesi Algoritmaları: R ve Python Uygulamaları Kitabındaki R ve Python kodları
tsdae
Tranformer-based Denoising AutoEncoder for Sentence Transformers Unsupervised pre-training.
ml-oneday-course
This is a one-day machine learning introductory course for beginners
https://github.com/chenliu-1996/cuts
[MICCAI 2024] CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
https://github.com/ammarlodhi255/ml_spelled_out
Collection of notebooks and python code of various machine learning algorithms from scratch.
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/ashrithsagar/cp217-ml4cps-2024
CP 217 Machine Learning for Cyber-Physical Systems, IISc
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