Deep learning research aims at discovering learning algorithms that discovermultiple levels of distributed representations, with higher levels representingmore abstract concepts. Although the study of deep learning has already led toimpressive theoretical results, learning algorithms and breakthroughexperiments, several challenges lie ahead. This paper proposes to examine someof these challenges, centering on the questions of scaling deep learningalgorithms to much larger models and datasets, reducing optimizationdifficulties due to ill-conditioning or local minima, designing more efficientand powerful inference and sampling procedures, and learning to disentangle thefactors of variation underlying the observed data. It also proposes a fewforward-looking research directions aimed at overcoming these challenges.
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