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Feature learning

Known as: Learning representation, Representation learning, Unsupervised feature learning 
In machine learning, feature learning or representation learning is a set of techniques that learn a feature: a transformation of raw data input to a… 
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Related topics

Related topics

33 relations
Artificial neural networkAutoencoderBasis functionBoltzmann machine

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Exploiting Structured News Information to Improve Event Detection via Dual-Level Clustering
With massive amount of news articles come to fore every seconds, helping people navigate through correlated news on a given topic… 
2018
2018
Joint Representation Learning for Location-Based Social Networks with Multi-Grained Sequential Contexts
This article studies the problem of learning effective representations for Location-Based Social Networks (LBSN), which is useful… 
2018
2018
Face Alignment across Large Pose via MT-CNN Based 3D Shape Reconstruction
Face alignment plays an important role for robust face recognition and analysis applications in the wild. While a number of face… 
2017
2017
Information Dropout: learning optimal representations through noise
We introduce Information Dropout, a generalization of dropout that is motivated by the Information Bottleneck principle and… 
2017
2017
PNE: Label Embedding Enhanced Network Embedding
Unsupervised NRL (Network Representation Learning) methods only consider the network structure information, which makes their… 
2016
2016
Visual Aesthetic Quality Assessment with Multi-task Deep Learning
This paper considers the problem of assessing visual aesthetic quality with semantic information. We cast the assessment problem… 
2016
2016
Inside Out: Two Jointly Predictive Models for Word Representations and Phrase Representations
Distributional hypothesis lies in the root of most existing word representation models by inferring word meaning from its… 
2016
2016
Simultaneous Learning of Trees and Representations for Extreme Classification, with Application to Language Modeling
This paper addresses the problem of multi-class classification with an extremely large number of classes, where the class predic… 
2015
2015
Using representation learning and out-of-domain data for a paralinguistic speech task
In this work, we study the paralinguistic speech task of eating condition classification and present our submitted clas-sification… 
2014
2014
Learning Multilingual Word Representations using a Bag-of-Words Autoencoder
Recent work on learning multilingual word representations usually relies on the use of word-level alignements (e.g. infered with…