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The Geometry of Adversarial Subspaces
We undertake analysis to characterize the geometry of the boundary, which is more curved within the adversarial subspace than within a random subspace of equal dimensionality.
The Sparse Manifold Transform
We present a signal representation framework called the sparse manifold transform that combines key ideas from sparse coding, manifold learning, and slow feature analysis.
Replicating Kernels with a Short Stride Allows Sparse Reconstructions with Fewer Independent Kernels
We find, for example, that for 16x16-pixel receptive fields, using eight kernels and a stride of 2 leads to sparse reconstructions of comparable quality as using 512 kernels and a stride of 16 (the nonoverlapping case).
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