Probabilistic Models for Automatic Equivariant Feature Discovery

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The performance of machine learning methods is often heavily dependent on how the input data is represented, making the choice of data representation a key aspect of many applications. The field of representation learning seeks to answer questions surrounding how we can best learn meaningful representations of data. Recent interest in applying deep models to representation learning has been fueled by successes in speech recognition, categorization of object in images and language tasks such as paraphrase detection.