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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 76–100 of 823 papers

TitleStatusHype
DNF: Unconditional 4D Generation with Dictionary-based Neural Fields—0
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions—0
Features that Make a Difference: Leveraging Gradients for Improved Dictionary Learning—0
Alternative Learning Paradigms for Image Quality Transfer—0
Beyond Label Attention: Transparency in Language Models for Automated Medical Coding via Dictionary Learning—0
Group Crosscoders for Mechanistic Analysis of Symmetry—0
Improving Neuron-level Interpretability with White-box Language Models—0
Quantifying Feature Space Universality Across Large Language Models via Sparse AutoencodersCode0
Convolutional Dictionary Learning Based Hybrid-Field Channel Estimation for XL-RIS-Aided Massive MIMO Systems—0
LASERS: LAtent Space Encoding for Representations with Sparsity for Generative Modeling—0
Fast Structured Orthogonal Dictionary Learning using Householder Reflections—0
Atom dimension adaptation for infinite set dictionary learning—0
Geometry of the Space of Partitioned Networks: A Unified Theoretical and Computational FrameworkCode0
Sparsifying Parametric Models with L0 RegularizationCode0
BINDy -- Bayesian identification of nonlinear dynamics with reversible-jump Markov-chain Monte-Carlo—0
Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning—0
A Model for Combinatorial Dictionary Learning and Inference—0
Subgraph Clustering and Atom Learning for Improved Image Classification—0
A Resolution Independent Neural Operator—0
Compressive Electron Backscatter Diffraction Imaging—0
Dataset Dictionary Learning in a Wasserstein Space for Federated Domain Adaptation—0
Variational Learning ISTA—0
Unmixing Noise from Hawkes Process to Model Learned Physiological Events—0
Unsupervised learning of Data-driven Facial Expression Coding System (DFECS) using keypoint tracking—0
CREIMBO: Cross-Regional Ensemble Interactions in Multi-view Brain Observations—0
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