1997年11月15日 · Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations.

2021年5月14日 · Furthermore, in this paper, we introduce an LSTM cell's architecture, and explain how different components go together to alter the cell's memory and predict the output. Also, the paper provides the necessary formulas and foundations to calculate a forward iteration through an LSTM.

1997年12月1日 · We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM).

2015年3月13日 · In this paper, we present the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling. The hyperparameters of all LSTM variants for each task were optimized separately using random search, and their importance was assessed using the powerful ...

补救措施。本文提出了一种新的递归网络结构——长短时记忆(lstm)，并结合适当的梯度学习算法。lstm的设计就是为了克服这些错误的反向问题。它可以学习桥接超过1000步的时间间隔，即使在有噪声、不可压缩的输入序列的情况下，也不会损失短时间延迟能力。

1997年11月1日 · We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow ...

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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…

2019年8月25日 · 论文作者借助大规模实验，利用声音识别、手写体字符识别、音乐识别（polyphonic music modeling）三项任务，评判vanilla LSTM及其八大变体的表现，借此找出LSTM各组件的作用。

2024年10月31日 · 在深度学习的广阔领域中，长短时记忆网络（Long Short-Term Memory, LSTM）无疑是一颗璀璨的明星。本项目提供了一篇关于LSTM的清晰版原始论文，旨在帮助读者深入理解这一关键技术。