Review on deep learning applications in frequency analysis and control of modern power system
The penetration of renewable energy resources (RES) generation and the interconnection of
regional power grids in wide area and large scale have led the modern power system to …
regional power grids in wide area and large scale have led the modern power system to …
An introductory review of deep learning for prediction models with big data
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and
machine learning. Recent breakthrough results in image analysis and speech recognition …
machine learning. Recent breakthrough results in image analysis and speech recognition …
A programmable diffractive deep neural network based on a digital-coding metasurface array
The development of artificial intelligence is typically focused on computer algorithms and
integrated circuits. Recently, all-optical diffractive deep neural networks have been created …
integrated circuits. Recently, all-optical diffractive deep neural networks have been created …
Promptagator: Few-shot dense retrieval from 8 examples
Much recent research on information retrieval has focused on how to transfer from one task
(typically with abundant supervised data) to various other tasks where supervision is limited …
(typically with abundant supervised data) to various other tasks where supervision is limited …
Graph neural networks for natural language processing: A survey
Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …
Language Processing (NLP). Although text inputs are typically represented as a sequence …
A review of recurrent neural networks: LSTM cells and network architectures
Y Yu, X Si, C Hu, J Zhang - Neural computation, 2019 - direct.mit.edu
Recurrent neural networks (RNNs) have been widely adopted in research areas concerned
with sequential data, such as text, audio, and video. However, RNNs consisting of sigma …
with sequential data, such as text, audio, and video. However, RNNs consisting of sigma …
Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network
A Sherstinsky - Physica D: Nonlinear Phenomena, 2020 - Elsevier
Because of their effectiveness in broad practical applications, LSTM networks have received
a wealth of coverage in scientific journals, technical blogs, and implementation guides …
a wealth of coverage in scientific journals, technical blogs, and implementation guides …
Machine remaining useful life prediction via an attention-based deep learning approach
For prognostics and health management of mechanical systems, a core task is to predict the
machine remaining useful life (RUL). Currently, deep structures with automatic feature …
machine remaining useful life (RUL). Currently, deep structures with automatic feature …
Adversarial attacks on deep-learning models in natural language processing: A survey
With the development of high computational devices, deep neural networks (DNNs), in
recent years, have gained significant popularity in many Artificial Intelligence (AI) …
recent years, have gained significant popularity in many Artificial Intelligence (AI) …
Deep learning for change detection in remote sensing images: Comprehensive review and meta-analysis
L Khelifi, M Mignotte - Ieee Access, 2020 - ieeexplore.ieee.org
Deep learning (DL) algorithms are considered as a methodology of choice for remote-
sensing image analysis over the past few years. Due to its effective applications, deep …
sensing image analysis over the past few years. Due to its effective applications, deep …