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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 401–450 of 823 papers

TitleStatusHype
Multi-Scale Saliency Detection using Dictionary Learning—0
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning—0
Multi-task additive models with shared transfer functions based on dictionary learning—0
Multi-task Dictionary Learning based Convolutional Neural Network for Computer aided Diagnosis with Longitudinal Images—0
Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images—0
Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A model-based deep learning approach—0
Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications—0
New Algorithms for Learning Incoherent and Overcomplete Dictionaries—0
New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning—0
NNK-Means: Data summarization using dictionary learning with non-negative kernel regression—0
Noise Level Estimation for Overcomplete Dictionary Learning Based on Tight Asymptotic Bounds—0
Noisy Inductive Matrix Completion Under Sparse Factor Models—0
Noisy Matrix Completion under Sparse Factor Models—0
Nonconvex Demixing From Bilinear Measurements—0
Nonlinear Functional Output Regression: a Dictionary Approach—0
Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation—0
Non-negative representation based discriminative dictionary learning for face recognition—0
Non-negative Tensor Patch Dictionary Approaches for Image Compression and Deblurring Applications—0
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations—0
NOODL: Provable Online Dictionary Learning and Sparse Coding—0
Novel min-max reformulations of Linear Inverse Problems—0
Uncovering Model Processing Strategies with Non-Negative Per-Example Fisher Factorization—0
Object Classification with Joint Projection and Low-rank Dictionary Learning—0
Observable dictionary learning for high-dimensional statistical inference—0
OnACID: Online Analysis of Calcium Imaging Data in Real Time—0
On Learning Sparsely Used Dictionaries from Incomplete Samples—0
Online Convolutional Dictionary Learning—0
Online Convolutional Dictionary Learning for Multimodal Imaging—0
Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems—0
Online Dictionary Learning for Approximate Archetypal Analysis—0
Online dictionary learning for kernel LMS. Analysis and forward-backward splitting algorithm—0
Online L1-Dictionary Learning with Application to Novel Document Detection—0
Online Low-Rank Subspace Learning from Incomplete Data: A Bayesian View—0
Online multidimensional dictionary learning—0
Online Multilinear Dictionary Learning—0
Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning—0
Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method—0
Online Robust Dictionary Learning—0
Online Unsupervised Feature Learning for Visual Tracking—0
On some provably correct cases of variational inference for topic models—0
On the Analysis of Multi-Channel Neural Spike Data—0
On the Computational Intractability of Exact and Approximate Dictionary Learning—0
On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution—0
On the Invariance of Dictionary Learning and Sparse Representation to Projecting Data to a Discriminative Space—0
On the Minimax Risk of Dictionary Learning—0
On the Preservation of Spatio-temporal Information in Machine Learning Applications—0
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions—0
On the relations of LFPs & Neural Spike Trains—0
On The Sample Complexity of Sparse Dictionary Learning—0
On the Transition from Neural Representation to Symbolic Knowledge—0
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