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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 471–480 of 823 papers

TitleStatusHype
Identifiability of Complete Dictionary Learning—0
Identification of refugee influx patterns in Greece via model-theoretic analysis of daily arrivals—0
Identifying global optimality for dictionary learning—0
Image Deraining via Self-supervised Reinforcement Learning—0
Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis—0
Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis—0
Improving Neuron-level Interpretability with White-box Language Models—0
Impulse Denoising From Hyper-Spectral Images: A Blind Compressed Sensing Approach—0
Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data—0
Interpretable Neural Embeddings with Sparse Self-Representation—0
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