“Long/Short-Term Memory (LSTM)” is a special “RNN” capable of learning long-term dependencies simulating in its feedback connections a “general purpose computer.” From: Applied Biomedical Engineering Using
文章介绍:Long Short-Term Memory 发表于期刊Neural computation(1997),Sepp Hochreiter, Jurgen Schmidhuber. 这篇文章是深度学习领域引用率最高的论文之一。文章缓解了RNN网络存在的梯度消失和梯度爆炸问题。R…
Results presented in this work demonstrated that muscle activity detection during gait can be successfully performed using the novel approach based on Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs). The newly introduced LSTM-MAD was proven to outperform the tested state-of-the-art...
The remedy. This paper presents \Long Short-Term Memory" (LSTM), a novel recurrent network architecture in conjunction with an appropriate gradient-based learning algorithm. LSTM is designed to overcome these error back- ow problems. It can learn to bridge time intervals in excess of 1000 steps...
深度学习是一种在人工智能领域中具有重要影响力的技术,它已经在各种任务中取得了显著的成果。而在深度学习算法中,长短期记忆网络(Long Short-Term Memory,LSTM)是一种特殊的循环神经网络(Recurrent Neural Network,RNN),它在序列数据建模中具有出色的能力。本文将深入探讨LSTM网络的原理和应用,以及它在深度学习领域的重...
LONG SHORT TERM MEMORY Memory cells and gate units To construct an architecture that allows for constant error ow through sp ecial selfconnected units without the disadvantages of the naive approach we extend the selfconnected linear unit j from Section by intro ducing additional features A ...
2. Long short-term memory networks LSTMNs are a special kind of the RNN that is capable of learning the long-term dependencies. The RNN has a form of a chain of repeating modules of neural network, as shown in Fig. 1 (a). The principle of an RNN is to use the sequential informatio...
memory cell 有一个循环自连接的权值为 1 的边,这样 memory cell state 中梯度沿时间传播时不会导致不会 vanishing 或者 exploding ,output gate 类似于 input gate 会产生一个 0-1 向量来控制 memory cell 到输出层的输出。即 vt=st⊙otvt=st⊙ot ...
This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market over the period ...
short-term features at the same time, and could not learn both temporal and spatial properties in one model. To handle this multi- dimensional multi-step prediction problem, we proposed a data- driven model, named Long Short-Term Memory - Fully Connected Neural Network, to predict PM 2.5 con...