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A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware
Nature Machine Intelligence, 2021Spike-based neuromorphic hardware holds promise for more energy-efficient implementations of deep neural networks (DNNs) than standard hardware such as GPUs.
Philipp Plank +3 more
semanticscholar +1 more source
2019
In this chapter, you will learn about recurrent neural networks and long short-term memory models. You will also learn how LSTMs work and how they can be used to detect anomalies and how you can implement anomaly detection using LSTM. You will work through several datasets depicting time series of different types of data such as CPU utilization, taxi ...
Sridhar Alla, Suman Kalyan Adari
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In this chapter, you will learn about recurrent neural networks and long short-term memory models. You will also learn how LSTMs work and how they can be used to detect anomalies and how you can implement anomaly detection using LSTM. You will work through several datasets depicting time series of different types of data such as CPU utilization, taxi ...
Sridhar Alla, Suman Kalyan Adari
openaire +2 more sources
Remaining Useful Life Prognosis Based on Ensemble Long Short-Term Memory Neural Network
IEEE Transactions on Instrumentation and Measurement, 2021Remaining useful life (RUL) prognosis is of great significance to improve the reliability, availability, and maintenance cost of an industrial equipment.
Yiwei Cheng +4 more
semanticscholar +1 more source
IEEE transactions on industrial electronics (1982. Print), 2020
Existing state-of-health (SOH) data-driven prediction techniques for lithium-ion batteries are subject to mass training data, which leads to limited application.
Yandan Tan, Guangcai Zhao
semanticscholar +1 more source
Existing state-of-health (SOH) data-driven prediction techniques for lithium-ion batteries are subject to mass training data, which leads to limited application.
Yandan Tan, Guangcai Zhao
semanticscholar +1 more source
Hardware aspects of Long Short Term Memory
2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2018This paper focuses on hardware implementation aspects of a Long-Short Term Memory (LSTM) network application. Initially, certain piecewise approximations to activation functions $\sigma$ and tanh are proposed. Next a training procedure is introduced which exploits proposed piecewise approximations.
Ioannis Kouretas, Vassilis Paliouras
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2012
As discussed in the previous chapter, an important benefit of recurrent neural networks is their ability to use contextual information when mapping between input and output sequences. Unfortunately, for standard RNN architectures, the range of context that can be in practice accessed is quite limited.
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As discussed in the previous chapter, an important benefit of recurrent neural networks is their ability to use contextual information when mapping between input and output sequences. Unfortunately, for standard RNN architectures, the range of context that can be in practice accessed is quite limited.
openaire +2 more sources
Long Short-Term Memory in Intelligent Buildings
2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE), 2020This paper presents Long Short-Term Memory (LSTM) in iBuilding: Artificial Intelligence in Intelligent Buildings. LSTM networks are widely used in time series data as their learning algorithm does not present exploding and vanishing gradient descent issues as traditional recurrent neural networks with back propagation learning algorithms.
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A Siamese Long Short-Term Memory Architecture for Human Re-identification
European Conference on Computer Vision, 2016Matching pedestrians across multiple camera views known as human re-identification (re-identification) is a challenging problem in visual surveillance.
Rahul Rama Varior +4 more
semanticscholar +1 more source
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
IEEE transactions on industrial electronics (1982. Print), 2018State of charge (SOC) estimation is critical to the safe and reliable operation of Li-ion battery packs, which nowadays are becoming increasingly used in electric vehicles (EVs), Hybrid EVs, unmanned aerial vehicles, and smart grid systems.
Ephrem Chemali +4 more
semanticscholar +1 more source
Learning and long- and short-term memory in cockroaches
Animal Behaviour, 1970Abstract Intact cockroaches, headless insects and isolated segments of the animal were trained to avoid electric shocks, contingent upon an electric signal which was applied a fraction of a second prior to the shocks. The results showed that all the intact insects learned to avoid the shocks in approximately ten sessions, on a schedule of two daily ...
J.V. Luco, W.Y. Chen, L.C. Aranda
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