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Unsupervised learning

Known as: Unsupervised approach, Unsupervised classification 
Unsupervised learning is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given… 
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Related topics

Related topics

49 relations
Anomaly detectionArtificial intelligence for video surveillanceAutoencoderAutomatic target recognition

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2018
Highly Cited
2018
Unsupervised Representation Learning by Predicting Image Rotations
Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their… 
Highly Cited
2015
Highly Cited
2015
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications… 
Highly Cited
2015
Highly Cited
2015
Unsupervised Learning of Video Representations using LSTMs
We use Long Short Term Memory (LSTM) networks to learn representations of video sequences. Our model uses an encoder LSTM to map… 
Highly Cited
2011
Highly Cited
2011
Reading Digits in Natural Images with Unsupervised Feature Learning
Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging… 
Highly Cited
2011
Highly Cited
2011
An Analysis of Single-Layer Networks in Unsupervised Feature Learning
A great deal of research has focused on algorithms for learning features from unlabeled data. Indeed, much progress has been made… 
Highly Cited
2011
Highly Cited
2011
Autoencoders, Unsupervised Learning, and Deep Architectures
  • P. Baldi
  • ICML Unsupervised and Transfer Learning
  • 2011
  • Corpus ID: 10921035
Autoencoders play a fundamental role in unsupervised learning and in deep architectures for transfer learning and other tasks. In… 
Highly Cited
2004
Highly Cited
2004
Unsupervised Learning by Probabilistic Latent Semantic Analysis
This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a… 
Highly Cited
2002
Highly Cited
2002
Unsupervised Learning of Finite Mixture Models
This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective… 
Highly Cited
1999
Highly Cited
1999
Unsupervised Learning
Unsupervised learning studies how systems can learn to represent particular input patterns in a way that reflects the statistical… 
Review
1999
Review
1999
Unsupervised Learning
  • H. Barlow
  • Neural Computation
  • 1999
  • Corpus ID: 38203858
What use can the brain make of the massive flow of sensory information that occurs without any associated rewards or punishments… 
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