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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 71–80 of 823 papers

TitleStatusHype
Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning—0
Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction—0
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly DetectionCode0
Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis—0
Image Deraining via Self-supervised Reinforcement Learning—0
Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial PoliciesCode0
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement LearningCode2
Dictionary Learning Improves Patch-Free Circuit Discovery in Mechanistic Interpretability: A Case Study on Othello-GPT—0
A Lightweight Randomized Nonlinear Dictionary Learning Method using Random Vector Functional Link—0
Seismic Traveltime Tomography with Label-free LearningCode0
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