We propose-Deep Simplex (KDS) which, given a set of data points, learns a dictionary
comprising synthetic landmarks, along with representation coefficients supported on a …

Wasserstein dictionary learning is an unsupervised approach to learning a collection of
probability distributions that generate observed distributions as Wasserstein barycentric …

We propose K-Deep Simplex (KDS) which, given a set of data points, learns a dictionary
comprising synthetic landmarks, along with representation coefficients supported on a …

The classical sparse coding model represents visual stimuli as a linear combination of a
handful of learned basis functions that are Gabor-like when trained on natural image data …

We pursue local sparse representations of data by considering a common data model where
representations are formed as a combination of atoms that we call a dictionary. Our focus is …

Sparse coding is a pillar of computational neuroscience, learning filters that well-describe
the sensitivities of mammalian simple cell receptive fields (SCRFs) in a least-squares sense …