Space–time signal binding in recurrent neural networks with controlled elements

Osipov, Vasiliy; Osipova, Marina

doi:10.1016/j.neucom.2018.05.009

Cited by 26 publications

(20 citation statements)

References 32 publications

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“…Ref. [51] proposed a four-layer model to show how BDA can help IOT-based system to work better. This model comprised of data generation, sensor communication, data processing, and data interpretation [51].…”

Section: Big Data Analytics and Internet Of Things (Iot)mentioning

confidence: 99%

Literature Review on Big Data Analytics Methods

Vanani¹,

Majidian²

2020

Social Media and Machine Learning

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Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed.

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Section: Big Data Analytics and Internet Of Things (Iot)mentioning

confidence: 99%

Literature Review on Big Data Analytics Methods

Vanani¹,

Majidian²

2020

Social Media and Machine Learning

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“…RNNs have gained attention due to their ability to model sequential tasks and data (Osipov & Osipova, 2018;Morchid, 2018) by allowing relevant information to persist throughout the sequence. RNNs have gained attention due to their ability to model sequential tasks and data (Osipov & Osipova, 2018;Morchid, 2018) by allowing relevant information to persist throughout the sequence.…”

Section: Bi-lstm Layermentioning

confidence: 99%

“…A recurrent neural network (RNN) is a network with loops. RNNs have gained attention due to their ability to model sequential tasks and data (Osipov & Osipova, 2018;Morchid, 2018) by allowing relevant information to persist throughout the sequence. However, existing RNN models and techniques are limited in their ability to handle instances with longer sentences because they may suffer from problems of vanishing or exploding gradients (Pascanu et al, 2012).…”

Section: Bi-lstm Layermentioning

confidence: 99%

GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning

Qiu

Xie

Tao

2019

Earth and Space Science

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A variety of detailed data about geological topics and geoscience knowledge are buried in the geoscience literature and rarely used. Named entity recognition (NER) provides both opportunities and challenges to leverage this wealth of data in the geoscience literature for data analysis and further information extraction. Existing NER models and techniques are mainly based on rule‐based and supervised approaches, and developing such systems requires a costly manual effort. In this paper, we first design a generic stepwise framework for domain‐specific NER. Following this framework, domain‐specific entities and domain‐general words are collected and selected as seed terms. Normalization and grouping processes are then applied to these seed terms for further analysis. A random extraction algorithm based on a unigram language model is used to generate a large‐scale training data set consisting of probabilistically labeled pseudosentences. Each generated sentence is then used as input to the self‐training and learning algorithm. Experimental results on two constructed data sets demonstrate that the proposed model effectively recognizes and identifies geological named entities.

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“…We also show that our method can learn powerful embeddings of input face images, which can be used to achieve facial attribute manipulation. Moreover we propose a multi-view feature extraction strategy to extract effective image representations, which can be used to achieve state of the art performance in facial attribute prediction.recovery [8,9,10], image privacy protection [11], unsupervised dimension reduction [12] and many other applications [13,14,15]. Deep convolutional generative models, as a branch of unsupervised learning technique in machine learning, have become an area of active research in recent years.…”

mentioning

confidence: 99%

“…recovery [8,9,10], image privacy protection [11], unsupervised dimension reduction [12] and many other applications [13,14,15]. Deep convolutional generative models, as a branch of unsupervised learning technique in machine learning, have become an area of active research in recent years.…”

mentioning

confidence: 99%

Improving variational autoencoder with deep feature consistent and generative adversarial training

et al. 2019

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We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output and its corresponding input images to have similar deep features, we also implement a generative adversarial training mechanism to force the VAE to output realistic and natural images. We present experimental results to show that the VAE trained with our new method outperforms state of the art in generating face images with much clearer and more natural noses, eyes, teeth, hair textures as well as reasonable backgrounds. We also show that our method can learn powerful embeddings of input face images, which can be used to achieve facial attribute manipulation. Moreover we propose a multi-view feature extraction strategy to extract effective image representations, which can be used to achieve state of the art performance in facial attribute prediction.recovery [8,9,10], image privacy protection [11], unsupervised dimension reduction [12] and many other applications [13,14,15]. Deep convolutional generative models, as a branch of unsupervised learning technique in machine learning, have become an area of active research in recent years. A generative model trained with a given image database can be useful in several ways. One is to learn the essence of a dataset and generate realistic images similar to those in the dataset from random inputs. The whole dataset is "compressed" into the learned parameters of the model, which are significantly smaller than the size of the training dataset. The other is to learn reusable feature representations from unlabeled image datasets for a variety of supervised learning tasks such as image classification.In this paper, we propose a new method to train the variational autoencoder (VAE)[16] to improve its performance. In particular, we seek to improve the quality of the generated images to make them more realistic and less blurry. To achieve this, we employ objective functions based on deep feature consistent principle [17] and generative adversarial network [18,19] instead of the problematic per-pixel loss functions. The deep feature consistent can help capture important perceptual features such as spatial correlation through the learned convolutional operations, while the adversarial training helps to produce images that reside on the manifold of natural images. We also introduce several techniques to improve the convergence of GAN training in this context. In particular, instead of directly using the generated images and the real images in pixel space, the corresponding deep features extracted from pretrained networks are used to train the generator and the discriminator network. We also propose to further relax the constraint on the output of the discriminator network to balance the image reconstruction loss and the adversarial loss. We present experimental results to show that our new method can generate face images with much clearer facial parts such as eyes, nose, mouth, teeth, ears and hair textur...

show abstract

Space–time signal binding in recurrent neural networks with controlled elements

Cited by 26 publications

References 32 publications

Literature Review on Big Data Analytics Methods

Literature Review on Big Data Analytics Methods

GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning

Improving variational autoencoder with deep feature consistent and generative adversarial training

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