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… (More)
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Papers overview

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Review
2017
Review
2017
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in… (More)
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Review
2017
Review
2017
Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the… (More)
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Highly Cited
2017
Highly Cited
2017
The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent… (More)
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Highly Cited
2016
Highly Cited
2016
Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms… (More)
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Highly Cited
2016
Highly Cited
2016
We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto… (More)
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Highly Cited
2016
Highly Cited
2016
NTechLAB facenx_large Google FaceNet v8 Beijing Faceall Co. FaceAll_Norm_1600 Beijing Faceall Co. FaceAll_1600 large 73.300% 70… (More)
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Highly Cited
2011
Highly Cited
2011
Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging… (More)
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Highly Cited
2011
Highly Cited
2011
Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio… (More)
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Highly Cited
2011
Highly Cited
2011
A great deal of research has focused on algorithms for learning features from unlabeled data. Indeed, much progress has been made… (More)
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Highly Cited
2007
Highly Cited
2007
We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of the well… (More)
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