Challenges in Representation Learning: A Report on Three Machine Learning Contests
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"Challenges in Representation Learni..." refers methods in this paper
...22% using blending of three models that used sparse filtering[5] for feature learning, random forests for feature selection [6], and support vector machines[7] for classification....
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...Their approach used SIFT [17] and MKL....
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"Challenges in Representation Learni..." refers background in this paper
...” The purpose of the workshop, organized by Ian Goodfellow, Dumitru Erhan, and Yoshua Bengio, was to explore the latest developments in representation learning, with a special emphasis on testing the capabilities of current representation learning algorithms (See [1] for a recent review) and pushing the field towards new developments via these contests....
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...Other top scorers included Jingjing Xie, Bing Xu and Zhang Chuang, who developed ensemble voting techniques for use with denoising autoencoders [11] and maxout networks [12]....
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