RETRACTED: ELSTM: An improved long short‐term memory network language model for sequence learning

Abstract

The gated structure of the long short‐term memory (LSTM) alleviates the defects of gradient disappearance and explosion in the recurrent neural network (RNN). It has received widespread attention in sequence learning such as text analysis. Although LSTM has good performance in handling remote dependencies, information loss often occurs in long‐distance transmission. We propose a new model called ELSTM based on the computational complexity and gradient dispersion in the traditional LSTM model. This model simplifies the input gate of LSTM, reduces some time complexity by reducing some components, and improves the output gate. By introducing the exponential linear unit activation layer, the problem of gradient dispersion is alleviated. Comparing the new model with multiple existing models, when predicting language sequences, the time used by the model has been greatly reduced, and the language confusion has been reduced, showing good performance.

Artificial intelligence-based spatio-temporal vision sensors: applications and prospects

“…(1) The asynchronous spatiotemporal pulse signal can be described as a spatiotemporal point process in terms of data distribution (Zhu Z. et al, 2022), and the theory of point process signal processing, learning and reasoning can be introduced (Remesh et al, 2019;Xiao et al, 2019;Ru et al, 2022); (2) Asynchronous spatiotemporal pulse signal is similar to point cloud in spatiotemporal structure, and deep learning can be used in the structure and method of point cloud network (Wang et al, 2021;Lin L. et al, 2023;Valerdi et al, 2023); (3) The pulse signal is regarded as the node of the graph model, and the graph model signal processing and learning theory can be used (Shen et al, 2022;Bok et al, 2023); (4) The timing advantage of the high temporal resolution of the asynchronous spatiotemporal pulse signal is to mine the temporal memory model (Zhu N. et al, 2021;Li et al, 2023) and learn from the brain-like visual signal processing mechanism (Wang X. et al, 2022).…”

“…Gong (Gong, 2021) accumulated the pulse stream output by DVS as image and speech signal and input it to the deep belief network for character recognition. In addition, Li et al (Li et al, 2023) used a fixed temporal length to accumulate image sequences and used LSTM to recognize moving characters. Cherskikh (Cherskikh, 2022) accumulated the pulse stream output by ATIS into images according to fixed time domain length or fixed pulse data, and used convolutional network to identify objects.…”

“…Data-driven end-to-end neural networks for specific visual tasks can be established. Mining mechanisms with temporal memory (Zhu R. et al, 2021;Li et al, 2023) spiking neural network (Wang et al, 2022e) for learning and reasoning.…”