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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 671–680 of 823 papers

TitleStatusHype
Regularized Spherical Polar Fourier Diffusion MRI with Optimal Dictionary Learning—0
Removing Rain From a Single Image via Discriminative Sparse Coding—0
Representation Learning and Recovery in the ReLU Model—0
Reprogramming Language Models for Molecular Representation Learning—0
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning—0
Revisit Dictionary Learning for Video Compressive Sensing under the Plug-and-Play Framework—0
Riemannian Coding and Dictionary Learning: Kernels to the Rescue—0
Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices—0
Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold—0
Riemannian stochastic optimization methods avoid strict saddle points—0
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