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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 26–50 of 823 papers

TitleStatusHype
Dictionary-Learning-Based Data Pruning for System Identification—0
Inversion of Magnetic Data using Learned Dictionaries and Scale SpaceCode0
Boosting Adversarial Robustness and Generalization with Structural Prior—0
Exploring the Limitations of Structured Orthogonal Dictionary Learning—0
Multi-field Visualization: Trait design and trait-induced merge trees—0
MIRE: Matched Implicit Neural Representations—0
A Zero-Shot Physics-Informed Dictionary Learning Approach for Sound Field ReconstructionCode0
Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models—0
DNF: Unconditional 4D Generation with Dictionary-based Neural Fields—0
Monet: Mixture of Monosemantic Experts for TransformersCode2
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
Efficient Dictionary Learning with Switch Sparse AutoencodersCode1
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
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