Long short-term memory (LSTM) has transformed both machine learning and
neurocomputing fields. According to several online sources, this model has improved …

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 …

Neuromorphic computing based on emerging devices could overcome the von Neumann
bottleneck—the restriction created by having to transfer data between memory and …

Abstract Long Short-Term Memory (LSTM) has shown significant performance on many real-
world applications due to its ability to capture long-term dependencies. In this paper, we …

Not all neural network architectures are created equal, some perform much better than
others for certain tasks. But how important are the weight parameters of a neural network …

Several variants of the long short-term memory (LSTM) architecture for recurrent neural
networks have been proposed since its inception in 1995. In recent years, these networks …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

This chapter provides the background material and literature review for supervised
sequence labelling. Section 2.1 briefly reviews supervised learning in general. Section 2.2 …

Sequence prediction and classification are ubiquitous and challenging problems in machine
learning that can require identifying complex dependencies between temporally distant …

Intelligent transport systems, efficient electric grids, and sensor networks for data collection
and analysis are some examples of the multiagent systems (MAS) that cooperate to achieve …