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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 501–525 of 823 papers

TitleStatusHype
LASERS: LAtent Space Encoding for Representations with Sparsity for Generative Modeling—0
Latent Dictionary Learning for Sparse Representation based Classification—0
Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction—0
Learning a collaborative multiscale dictionary based on robust empirical mode decomposition—0
Learning a Common Dictionary for CSI Feedback in FDD Massive MU-MIMO-OFDM Systems—0
Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems—0
Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization—0
Learning a Pedestrian Social Behavior Dictionary—0
Learning Based Segmentation of CT Brain Images: Application to Post-Operative Hydrocephalic Scans—0
Learning Better Encoding for Approximate Nearest Neighbor Search with Dictionary Annealing—0
Learning brain regions via large-scale online structured sparse dictionary learning—0
Learning Class Prototypes via Structure Alignment for Zero-Shot Recognition—0
Learning computationally efficient dictionaries and their implementation as fast transforms—0
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries—0
Learning differentiable solvers for systems with hard constraints—0
Learning Discriminative ab-Divergences for Positive Definite Matrices—0
Learning Discriminative Alpha-Beta-divergence for Positive Definite Matrices (Extended Version)—0
Learning Discriminative Latent Attributes for Zero-Shot Classification—0
Learning efficient structured dictionary for image classification—0
Learning _1-based analysis and synthesis sparsity priors using bi-level optimization—0
Learning Fast Sparsifying Transforms—0
Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction—0
Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space—0
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit—0
Learning Invariant Subspaces of Koopman Operators--Part 2: Heterogeneous Dictionary Mixing to Approximate Subspace Invariance—0
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