Live briefing
Everything about Unsupervised learning
Paradigm in machine learning that uses no classification labels
Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. Other frameworks in the spectrum of supervisions include weak- or semi-supervision, where a small portion of the data is tagged, and self-supervision. Some researchers consider self-supervised learning a form of unsupervised learning. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Unsupervised learningA 5-minute orientation
- Skim a recent paper: Simple Unsupervised Graph Representation LearningInfluential work since 2022
- Pick up a classic book: Signal Processing and Machine Learning for Brain-Machine InterfacesThe most-reprinted book on the subject
- Read the landmark paper: Unsupervised LearningCited 941 times
- Go sideways: Restricted Boltzmann machineA closely related topic
- Go sideways: Helmholtz machineA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Unsupervised Learning
Unsupervised Learning for Passive Beamforming
Unsupervised learning of natural languages
Unsupervised learning of human motion
Unsupervised learning in noise
Image smoothing via unsupervised learning
Unsupervised meta-learning for few-shot learning
Unsupervised Learning of Morphology
🆕 Influential since 2022
Simple Unsupervised Graph Representation Learning
Lessons from infant learning for unsupervised machine learning
Collaborative Unsupervised Multi-View Representation Learning
Practical Vertical Federated Learning With Unsupervised Representation Learning
Optimal Transport for Unsupervised Denoising Learning
📚 Books worth reading
Signal Processing and Machine Learning for Brain-Machine InterfacesMahnaz Arvaneh · 2018
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Restricted Boltzmann machine A restricted Boltzmann machine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little…
- Helmholtz machine The Helmholtz machine (named after Hermann von Helmholtz and his concept of Helmholtz free energy) is a type of artificial neural network that can account…
- Boltzmann machine A Boltzmann machine (also called Sherrington–Kirkpatrick model with external field or stochastic Ising model), named after Ludwig Boltzmann, is a spin-glass…
- Wake-sleep algorithm The wake-sleep algorithm is an unsupervised learning algorithm for deep generative models, especially Helmholtz Machines. The algorithm is similar to the…
- Representation learning In machine learning (ML), representation learning or feature learning is a set of techniques that allow a system to automatically discover the representations…
- Deep belief network In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple…
- Types of artificial neural networks Types of neural networks (NN) include a family of techniques. The simplest types have static components, including number of units, number of layers, unit…
- Autoencoder An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns…