[HTML][HTML] Recurrent neural networks: A comprehensive review of architectures, variants, and applications
Recurrent neural networks (RNNs) have significantly advanced the field of machine learning
(ML) by enabling the effective processing of sequential data. This paper provides a …
(ML) by enabling the effective processing of sequential data. This paper provides a …
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 …
Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning
Video clip retrieval and captioning tasks play an essential role in multimodal research and
are the fundamental research problem for multimodal understanding and generation. The …
are the fundamental research problem for multimodal understanding and generation. The …
[HTML][HTML] Predicting stock market index using LSTM
The rapid advancement in artificial intelligence and machine learning techniques,
availability of large-scale data, and increased computational capabilities of the machine …
availability of large-scale data, and increased computational capabilities of the machine …
Clip4clip: An empirical study of clip for end to end video clip retrieval
Video-text retrieval plays an essential role in multi-modal research and has been widely
used in many real-world web applications. The CLIP (Contrastive Language-Image Pre …
used in many real-world web applications. The CLIP (Contrastive Language-Image Pre …
Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential
for safe and efficient operation of connected automated vehicles under complex driving …
for safe and efficient operation of connected automated vehicles under complex driving …
Understanding LSTM--a tutorial into long short-term memory recurrent neural networks
RC Staudemeyer, ER Morris - arxiv preprint arxiv:1909.09586, 2019 - arxiv.org
Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) are one of the most
powerful dynamic classifiers publicly known. The network itself and the related learning …
powerful dynamic classifiers publicly known. The network itself and the related learning …
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 …
[HTML][HTML] A state-of-the-art survey on deep learning theory and architectures
In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …
domains. This new field of machine learning has been growing rapidly and has been …
Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms
Abstract Fault Detection and Diagnosis (FDD) of rotating machinery plays a key role in
reducing the maintenance costs of the manufacturing systems. How to improve the FDD …
reducing the maintenance costs of the manufacturing systems. How to improve the FDD …