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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 101–110 of 823 papers

TitleStatusHype
Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis—0
Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control—0
Efficient Matrix Factorization Via Householder Reflections—0
Lightweight Conceptual Dictionary Learning for Text Classification Using Information Compression—0
Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal TransportCode0
Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning—0
Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction—0
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly DetectionCode0
Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis—0
Image Deraining via Self-supervised Reinforcement Learning—0
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