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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 151–175 of 823 papers

TitleStatusHype
A Riemannian ADMMCode0
Simple Alternating Minimization Provably Solves Complete Dictionary Learning—0
Geometric Sparse Coding in Wasserstein Space—0
Dictionary Learning for the Almost-Linear Sparsity RegimeCode0
Hybrid mmWave MIMO Systems under Hardware Impairments and Beam Squint: Channel Model and Dictionary Learning-aided Configuration—0
Unsupervised Opinion Summarization Using Approximate Geodesics—0
Self-Supervised Texture Image Anomaly Detection By Fusing Normalizing Flow and Dictionary Learning—0
Unitary Approximate Message Passing for Matrix Factorization—0
Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada—0
Learning Sparsity-Promoting Regularizers using Bilevel Optimization—0
Learning differentiable solvers for systems with hard constraints—0
Temporal Forward-Backward Consistency, Not Residual Error, Measures the Prediction Accuracy of Extended Dynamic Mode Decomposition—0
Deep Dictionary Learning with An Intra-class Constraint—0
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG SurveillanceCode1
Recent Results of Energy Disaggregation with Behind-the-Meter Solar Generation—0
Vector Quantisation for Robust SegmentationCode1
Supervised Dictionary Learning with Auxiliary CovariatesCode0
Convolutional Dictionary Learning by End-To-End Training of Iterative Neural NetworksCode0
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamicsCode1
Poisson2Sparse: Self-Supervised Poisson Denoising From a Single ImageCode1
Denoising Fast X-Ray Fluorescence Raster Scans of Paintings—0
Mixed noise reduction via sparse error constraint representation of high frequency image for wildlife imageCode1
Dictionary Learning with Accumulator Neurons—0
DEMAND: Deep Matrix Approximately Nonlinear Decomposition to Identify Meta, Canonical, and Sub-Spatial Pattern of functional Magnetic Resonance Imaging in the Human Brain—0
Gabor is Enough: Interpretable Deep Denoising with a Gabor Synthesis Dictionary PriorCode1
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