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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 476–500 of 823 papers

TitleStatusHype
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free ApproachCode0
Demystifying overcomplete nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization—0
Travel time tomography with adaptive dictionaries—0
Image Super-resolution via Feature-augmented Random ForestCode0
Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary—0
Learning Based Segmentation of CT Brain Images: Application to Post-Operative Hydrocephalic Scans—0
Noise Level Estimation for Overcomplete Dictionary Learning Based on Tight Asymptotic Bounds—0
Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification—0
OnACID: Online Analysis of Calcium Imaging Data in Real Time—0
Alternating minimization for dictionary learning with random initialization—0
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders—0
STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery—0
Alternating minimization for dictionary learning: Local Convergence Guarantees—0
Concave losses for robust dictionary learning—0
Robust Photometric Stereo via Dictionary Learning—0
Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics—0
Deep Self-taught Learning for Remote Sensing Image Classification—0
Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer—0
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video DenoisingCode0
Learning Discriminative Latent Attributes for Zero-Shot Classification—0
Learning Discriminative ab-Divergences for Positive Definite Matrices—0
Robust Surface Reconstruction from Gradients via Adaptive Dictionary Regularization—0
Robust Photometric Stereo Using Learned Image and Gradient Dictionaries—0
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled DictionariesCode0
Learning quadrangulated patches for 3D shape parameterization and completion—0
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