SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 823 papers

TitleStatusHype
Evolving Dictionary Representation for Few-shot Class-incremental Learning—0
Learning Dictionaries from Physical-Based Interpolation for Water Network Leak LocalizationCode0
Toward Real-Time Image Annotation Using Marginalized Coupled Dictionary LearningCode0
Convergence of alternating minimisation algorithms for dictionary learning—0
A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU—0
Cloud K-SVD for Image DenoisingCode0
X-ray Spectral Estimation using Dictionary Learning—0
Hiding Data Helps: On the Benefits of Masking for Sparse CodingCode0
Sparse, Geometric Autoencoder Models of V1—0
An Efficient Approximate Method for Online Convolutional Dictionary LearningCode0
A variational autoencoder-based nonnegative matrix factorisation model for deep dictionary learning—0
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning—0
Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications—0
Optimal Regularization for a Data Source—0
Learning Invariant Subspaces of Koopman Operators--Part 1: A Methodology for Demonstrating a Dictionary's Approximate Subspace Invariance—0
Learning Invariant Subspaces of Koopman Operators--Part 2: Heterogeneous Dictionary Mixing to Approximate Subspace Invariance—0
Matrix factorization with neural networks—0
Learning a Pedestrian Social Behavior Dictionary—0
Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A model-based deep learning approach—0
New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning—0
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals—0
Spiking sampling network for image sparse representation and dynamic vision sensor data compression—0
Decentralized Complete Dictionary Learning via ^4-Norm Maximization—0
A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy—0
Beyond Codebook-Based Analog Beamforming at mmWave: Compressed Sensing and Machine Learning Methods—0
Show:102550
← PrevPage 6 of 33Next →

No leaderboard results yet.