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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 281–290 of 823 papers

TitleStatusHype
Beyond Label Attention: Transparency in Language Models for Automated Medical Coding via Dictionary Learning—0
Deep Double Sparsity Encoder: Learning to Sparsify Not Only Features But Also Parameters—0
Beyond Codebook-Based Analog Beamforming at mmWave: Compressed Sensing and Machine Learning Methods—0
Analysis of Fast Alternating Minimization for Structured Dictionary Learning—0
Deep Dictionary Learning with An Intra-class Constraint—0
Deep Dictionary Learning: A PARametric NETwork Approach—0
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution—0
Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories—0
Bayesian sparsity and class sparsity priors for dictionary learning and coding—0
ANALYSIS OF CALIBRATED SEA CLUTTER AND BOAT REFLECTIVITY DATA AT C- AND X-BAND IN SOUTH AFRICAN COASTAL WATERS—0
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