Unsupervised Learning of Invariant Representations in Hierarchical Architectures

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Equivariance Through Parameter-Sharing (2017, methodology): “Going beyond augmentation, several methods directly apply the group-action, in one way or another, by transforming the data or its encodings using group members (Jaderberg et al., 2015; Anselmi et al., 2013; Dieleman et al., 2016).”
On Invariance and Selectivity in Representation Learning (2015, background): “Sci. and arXiv:1311.4158; Anselmi et al., 2013, Magic materials: a theory of deep hierarchical architectures for learning sensory representations.”
Deep Convolutional Networks are Hierarchical Kernel Machines (2015, methodology): “Then we establish some of their mathematical properties by using itheory as described in [8, 9, 10].”
Deep Haar Scattering Networks (2015, background): “Hierarchical unsupervised architectures have been shown to provide efﬁcient learning strategies [1].”