Attention in Long Short-Term Memory Recurrent Neural Networks
Sep 27, 2022 — Attention is the idea of freeing the encoder-decoder architecture from the fixed-length internal representation. This is achieved by keeping the intermediate outputs from the encoder LSTM from each step of the input sequence and training the model to learn to pay selective attention to these inputs and relate them to items in the output sequence.
A Novel CNN-based Bi-LSTM parallel model with attention
May 12, 2022 — In this paper, a 1-D Convolution Neural Network (CNN)-based bi-directional Long Short-Term Memory (LSTM) parallel model with attention mechanism (ConvBLSTM-PMwA) is proposed.
Guide to Bi-LSTM With Attention Mechanism - Analytics India …
Aug 1, 2024 — Guide to Bi-LSTM With Attention Mechanism. Adding Attention layer in any LSTM or Bi-LSTM can improve the performance of the model and also helps in making prediction in a accurate sequence. very helpful in NLP modeling with big data. Share.
Understanding the Property of Long Term Memory for the LSTM …
Oct 30, 2021 — We present a theoretical analysis of LSTM integrated with attention mechanism, and demonstrate that it is capable of generating an adaptive decay rate which dynamically controls the memory decay according to the obtained attention score.
Fusing convolutional learning and attention-based Bi-LSTM
Oct 29, 2024 — As illustrated in Fig. 6, ATBiLSTM surpassed other established models, including LSTM, BiLSTM, and LSTM-Attention, in classifying AD and MCI across different EEG samples. This suggests that ...
Tuning attention based long-short term memory neural networks …
Feb 21, 2024 — This study explores the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson’s disease based on dual-task walking test data.
Sep 23, 2019 — Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) are one of the most powerful dynamic classi ers publicly known. The net-work itself and the related learning algorithms are reasonably well docu-mented to get an idea how it works.
ARN-LSTM: A Multi-Stream Attention-Based Model for Action …
Nov 5, 2024 — This paper presents ARN-LSTM, a novel multi-stream action recognition model designed to address the challenge of simultaneously capturing spatial motion and temporal dynamics in action sequences. Traditional methods often focus solely on spatial or temporal features, limiting their ability to comprehend complex human activities fully. Our proposed …
Improving time series forecasting using LSTM and attention …
To forecast a given time series accurately, a hybrid model based on two deep learning methods, i.e., long short-term memory (LSTM) and multi-head attention is proposed in this study. The proposed method leverages the two learned representations from these techniques.
The rst enhance-ment, a mechanism known as attention [1, 11], allows the network to focus on more salient sequences within the LSTM memory space. Speci cally, in this dis-cussion, we examine self-attention, also known as intra-attention, which focuses on the important relationships between features within sequences [25, 10].
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