New Directions in Representation Learning
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Although machine learning is a powerful tool for artificial intelligence and data mining problems, the quality of the feature representations has been a critical limiting factor in success of machine learning systems. To address this problem, representation learning algorithms have recently emerged as ways to learn feature hierarchies from unlabeled and labeled data. In this talk, I will present my perspectives on the progress and challenges, as well as my recent related work on some new problems: (1) output representation learning for structured output prediction, (2) weakly supervised representation learning, (3) disentangling factors of variation with deep generative models.