Results 61 to 70 of about 572,458 (282)

Slice-Based Online Convolutional Dictionary Learning

IEEE Transactions on Cybernetics, 2021
Convolutional dictionary learning (CDL) aims to learn a structured and shift-invariant dictionary to decompose signals into sparse representations. While yielding superior results compared to traditional sparse coding methods on various signal and image processing tasks, most CDL methods have difficulties handling large data, because they have to ...
Yijie Zeng, Jichao Chen, Guang-Bin Huang
openaire   +3 more sources

Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals [PDF]

, 2013
Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures.
Bénar, Christian, Clerc, Maureen, Gramfort, Alexandre, Hitziger, Sebastian, Papadopoulo, Théodore, Saillet, Sandrine   +5 more
core   +4 more sources

A Perspective on Interactive Theorem Provers in Physics

Advanced Science, EarlyView.
Into an interactive theorem provers (ITPs), one can write mathematical definitions, theorems and proofs, and the correctness of those results is automatically checked. This perspective goes over the best usage of ITPs within physics and motivates the open‐source community run project PhysLean, the aim of which is to be a library for digitalized physics
Joseph Tooby‐Smith
wiley   +1 more source

Explicit Shift-Invariant Dictionary Learning [PDF]

IEEE Signal Processing Letters, 2014
In this letter we give efficient solutions to the construction of structured dictionaries for sparse representations. We study circulant and Toeplitz structures and give fast algorithms based on least squares solutions. We take advantage of explicit circulant structures and we apply the resulting algorithms to shift-invariant learning scenarios ...
Rusu, Cristian, Dumitrescu, Bogdan, Tsaftaris, Sotirios A.   +2 more
openaire   +2 more sources

Learning computationally efficient dictionaries and their implementation as fast transforms [PDF]

, 2015
Dictionary learning is a branch of signal processing and machine learning that aims at finding a frame (called dictionary) in which some training data admits a sparse representation. The sparser the representation, the better the dictionary.
Gribonval, Rémi, Magoarou, Luc Le
core   +3 more sources

High‐Conductivity Electrolytes Screened Using Fragment‐ and Composition‐Aware Deep Learning

Advanced Science, EarlyView.
We present a new deep learning framework that hierarchically links molecular and functional unit attributions to predict electrolyte conductivity. By integrating molecular composition, ratios, and physicochemical descriptors, it achieves accurate, interpretable predictions and large‐scale virtual screening, offering chemically meaningful insights for ...
Xiangwen Wang, Muyang Chen, Gengyi Bao, Yan Lai, Jinghe Cao, Xinhua Liu, Rui Tan   +6 more
wiley   +1 more source

Sparse representation for massive MIMO satellite channel based on joint dictionary learning

Electronics Letters
A constrained joint dictionary learning (CJDL) algorithm for high‐precision channel representation in massive multiple input multiple output (MIMO) satellite systems is proposed.
Qing yang Guan, Shuang Wu
doaj   +1 more source

SPD Data Dictionary Learning Based on Kernel Learning and Riemannian Metric

IEEE Access, 2020
The use of regional covariance descriptors to generate feature data represented by Symmetric Positive Definite (SPD) matrices from images or videos has become increasingly common in machine learning.
Rixin Zhuang, Zhengming Ma, Weijia Feng, Yuanping Lin   +3 more
doaj   +1 more source

Double-Norm Constrained Image Denoising Algorithm Based on Dictionary Learning Sparsity and FCM Structure Clustering

IEEE Access, 2022
To solve the problem of image smoothness and fuzzy edge texture information after image denoising, proposed a new image denoising method based on dictionary learning.
Changpeng Ji, Lina He, Wei Dai
doaj   +1 more source

Dictionary Learning Over Distributed Models [PDF]

IEEE Transactions on Signal Processing, 2015
In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements. This formulation is relevant in Big Data scenarios where large dictionary models may be spread over different spatial locations and it is not feasible to aggregate all dictionaries in one location ...
Chen, Jianshu, Towfic, Zaid J., Sayed, Ali H.   +2 more
openaire   +2 more sources