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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 451–475 of 823 papers

TitleStatusHype
Dictionary Learning by Dynamical Neural Networks—0
Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories—0
Dictionary Learning and Sparse Coding on Statistical Manifolds—0
Structured Analysis Dictionary Learning for Image ClassificationCode0
Compressed Dictionary Learning—0
On Learning Sparsely Used Dictionaries from Incomplete Samples—0
Towards Learning Sparsely Used Dictionaries with Arbitrary Supports—0
Multi-focus Image Fusion using dictionary learning and Low-Rank RepresentationCode0
Dictionary learning -- from local towards global and adaptive—0
Learning Simple Thresholded Features with Sparse Support Recovery—0
Binary Matrix Factorization via Dictionary Learning—0
White matter fiber analysis using kernel dictionary learning and sparsity priors—0
Cross-Domain Visual Recognition via Domain Adaptive Dictionary Learning—0
Multimodal Sparse Bayesian Dictionary Learning—0
Representation Learning and Recovery in the ReLU Model—0
Deep Dictionary Learning: A PARametric NETwork Approach—0
Unsupervised Band Selection of Hyperspectral Images via Multi-dictionary Sparse Representation—0
Convolutional Analysis Operator Learning: Acceleration and ConvergenceCode0
Information Assisted Dictionary Learning for fMRI data analysisCode0
Weakly-supervised Dictionary Learning—0
Analysis of Fast Alternating Minimization for Structured Dictionary Learning—0
Learning Light Field Reconstruction from a Single Coded Image—0
Robust Kronecker Component Analysis—0
Denoising Dictionary Learning Against Adversarial Perturbations—0
Frame-based Sparse Analysis and Synthesis Signal Representations and Parseval K-SVD—0
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