Dictionary Learning
152 papers with code • 0 benchmarks • 6 datasets
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
Benchmarks
These leaderboards are used to track progress in Dictionary Learning
Libraries
Use these libraries to find Dictionary Learning models and implementations
Extended Yale B
Partial-REID
SALSA
FMD
Partial-iLIDS
LoDoPaB-CT
Latest papers
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly Detection
We introduce a new anomaly detection model that unifies the OC-SVM and DL residual functions into a single composite objective, subsequently solved through K-SVD-type iterative algorithms.
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tokamak fusion reactor or minimizing the drag force exerted on an object in a fluid flow.
Seismic Traveltime Tomography with Label-free Learning
To avoid generating and/or collecting labeled samples, we propose a novel method by integrating deep learning and dictionary learning to enhance the VMs with low resolution by using the traditional tomography-least square method (LSQR).
Interpretable Online Network Dictionary Learning for Inferring Long-Range Chromatin Interactions
First, we demonstrate that online cvxNDL retains the accuracy of classical DL methods while simultaneously ensuring unique interpretability and scalability.
Orthogonally weighted $\ell_{2,1}$ regularization for rank-aware joint sparse recovery: algorithm and analysis
We propose and analyze an efficient algorithm for solving the joint sparse recovery problem using a new regularization-based method, named orthogonally weighted $\ell_{2, 1}$ ($\mathit{ow}\ell_{2, 1}$), which is specifically designed to take into account the rank of the solution matrix.
SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification
In this paper, we proposed a sparsely coded MIL (SC-MIL) that addresses those two aspects at the same time by leveraging sparse dictionary learning.
Uncovering hidden geometry in Transformers via disentangling position and context
jiajunsong629/uncover-hidden-geometry
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Given embedding vector $\boldsymbol{h}_{c, t} \in \mathbb{R}^d$ at sequence position $t \le T$ in a sequence (or context) $c \le C$, extracting the mean effects yields the decomposition \[ \boldsymbol{h}_{c, t} = \boldsymbol{\mu} + \mathbf{pos}_t + \mathbf{ctx}_c + \mathbf{resid}_{c, t} \] where $\boldsymbol{\mu}$ is the global mean vector, $\mathbf{pos}_t$ and $\mathbf{ctx}_c$ are the mean vectors across contexts and across positions respectively, and $\mathbf{resid}_{c, t}$ is the residual vector.
Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space
Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions.
Combating the Curse of Multilinguality in Cross-Lingual WSD by Aligning Sparse Contextualized Word Representations
begab/sparsity_makes_sense
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NAACL 2022
In this paper, we advocate for using large pre-trained monolingual language models in cross lingual zero-shot word sense disambiguation (WSD) coupled with a contextualized mapping mechanism.
Anomaly Detection with Selective Dictionary Learning
In this paper we present new methods of anomaly detection based on Dictionary Learning (DL) and Kernel Dictionary Learning (KDL).
