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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 111–120 of 823 papers

TitleStatusHype
Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial PoliciesCode0
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
Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems—0
Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization—0
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit—0
Interpretable Online Network Dictionary Learning for Inferring Long-Range Chromatin InteractionsCode0
Explainable Trajectory Representation through Dictionary Learning—0
Clustering Inductive Biases with Unrolled Networks—0
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