Autoencoder for words

Liou, Cheng-Yuan; Cheng, Wei-Chen; Liou, Jiun-Wei; Liou, Daw-Ran

doi:10.1016/j.neucom.2013.09.055

Cited by 427 publications

(189 citation statements)

References 7 publications

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“…An autoencoder is an unsupervised ANN that learns both linear and non-linear relationships present in data and represents them in a new reduced dimension data space (which also can be used to regenerate the original data space) without losing important information [34][35][36]. The autoencoder has two parts, the encoder part where an original dataset is encoded to a desired reduced feature space (encoded dataset) and the decoder part where the encoded dataset is decoded to an original dataset to determine how accurately the encoded dataset represents the original dataset.…”

Section: Model Training and Testingmentioning

confidence: 99%

An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

Milali

Kiware

Govella

et al. 2020

Preprint

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Background: After mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases to humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which currently are used to determine the parity status of mosquitoes, are very tedious and limited to very few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes. Methods and results: In this study, we train artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae

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Section: Model Training and Testingmentioning

confidence: 99%

An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

Milali

Kiware

Govella

et al. 2020

Preprint

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“…The implemented neural network is being trained to learn features and produce it as its output rather than generating classes in case of recalling the classification ability of the hired neural network [22]. The encoder input is the represented data while its output is the features learnt by autoencoder.…”

Section: Sparse Auto-encodermentioning

confidence: 99%

Auto-Encoder based Deep Learning for Surface Electromyography Signal Processing

Ibrahim¹,

Al-Jumaily²

2018

Adv. sci. technol. eng. syst. j.

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“…Basically, an autoencoder is an MLP or feedforward net with input and output tied together. This way, an autoencoder is supposed to be able to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction [34].…”

Section: Deep Belief Networkmentioning

confidence: 99%

Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform

Sugiarto

Pasila

2018

MATEC Web Conf.

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Abstract. Deep learning (DL) has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. Conceptually, it relies on a many-layer network that exhibits a hierarchically non-linear processing capability. Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence. However, many researchers are still sceptical about its true capability: can the intelligence demonstrated by deep learning technique be applied for general tasks? This question motivates the emergence of another research discipline: neuromorphic computing (NC). In NC, researchers try to identify the most fundamental ingredients that construct intelligence behaviour produced by the brain itself. To achieve this, neuromorphic systems are developed to mimic the brain functionality down to cellular level. In this paper, a neuromorphic platform called SpiNNaker is described and evaluated in order to understand its potential use as a platform for a deep learning approach. This paper is a literature review that contains comparative study on algorithms that have been implemented in SpiNNaker.

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Autoencoder for words

Cited by 427 publications

References 7 publications

An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

Auto-Encoder based Deep Learning for Surface Electromyography Signal Processing

Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform

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