Dictionary Learning
152 papers with code • 0 benchmarks • 6 datasets
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
Benchmarks
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Libraries
Use these libraries to find Dictionary Learning models and implementations
Extended Yale B
Partial-REID
SALSA
FMD
Partial-iLIDS
LoDoPaB-CT
Latest papers with no code
Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning
A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation.
Multi-Branch Generative Models for Multichannel Imaging with an Application to PET/CT Joint Reconstruction
This paper presents a proof-of-concept approach for learned synergistic reconstruction of medical images using multi-branch generative models.
Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis
This paper presents two methodological advancements in decentralized multi-task learning under privacy constraints, aiming to pave the way for future developments in next-generation Blockchain platforms.
Image Deraining via Self-supervised Reinforcement Learning
The work aims to recover rain images by removing rain streaks via Self-supervised Reinforcement Learning (RL) for image deraining (SRL-Derain).
Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial Policies
Optimal control of parametric partial differential equations (PDEs) is crucial in many applications in engineering and science.
Dictionary Learning Improves Patch-Free Circuit Discovery in Mechanistic Interpretability: A Case Study on Othello-GPT
Sparse dictionary learning has been a rapidly growing technique in mechanistic interpretability to attack superposition and extract more human-understandable features from model activations.
An SVD-free Approach to Nonlinear Dictionary Learning based on RVFL
The proposed RVFL-based nonlinear Dictionary Learning (RVFLDL) learns a dictionary as a sparse-to-dense feature map from nonlinear sparse coefficients to the dense input features.
Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems
In inverse problems, it is widely recognized that the incorporation of a sparsity prior yields a regularization effect on the solution.
Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization
Block majorization-minimization (BMM) is a simple iterative algorithm for nonconvex optimization that sequentially minimizes a majorizing surrogate of the objective function in each block coordinate while the other block coordinates are held fixed.
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit
To solve the optimization problem, we propose a new query dictionary learning algorithm inspired by classical sparse dictionary learning.
