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Dictionary Learning

Dictionary Learning is an important problem in multiple areas, ranging from computational neuroscience, machine learning, to computer vision and image processing. The general goal is to find a good basis for given data. More formally, in the Dictionary Learning problem, also known as sparse coding, we are given samples of a random vector $y\in\mathbb{R}^n$, of the form $y=Ax$ where $A$ is some unknown matrix in $\mathbb{R}^{n×m}$, called dictionary, and $x$ is sampled from an unknown distribution over sparse vectors. The goal is to approximately recover the dictionary $A$.

Source: Polynomial-time tensor decompositions with sum-of-squares

Papers

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Showing 441–450 of 823 papers

TitleStatusHype
On the Analysis of Multi-Channel Neural Spike Data—0
On the Computational Intractability of Exact and Approximate Dictionary Learning—0
On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution—0
On the Invariance of Dictionary Learning and Sparse Representation to Projecting Data to a Discriminative Space—0
On the Minimax Risk of Dictionary Learning—0
On the Preservation of Spatio-temporal Information in Machine Learning Applications—0
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions—0
On the relations of LFPs & Neural Spike Trains—0
On The Sample Complexity of Sparse Dictionary Learning—0
On the Transition from Neural Representation to Symbolic Knowledge—0
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