Memory Network Architecture for Packet Processing in Functions Virtualization

2021 ◽  
Author(s):  
Tomohiro Korikawa ◽  
Eiji Oki
Keyword(s):  
Network Architecture ◽  
Packet Processing ◽  
Memory Network

A two-layer memory network architecture for a pattern classifier

1983 ◽  
Vol 1 (4) ◽  
pp. 267-271 ◽  
Author(s):  
M.C Fairhurst ◽  
M.A.G Mattoso Maia
Keyword(s):  
Network Architecture ◽  
Memory Network ◽  
Pattern Classifier

scholarly journals Mention Recommendation for Twitter with End-to-end Memory Network

2017 ◽  
Author(s):  
Haoran Huang ◽  
Qi Zhang ◽  
Xuanjing Huang
Keyword(s):  
Statistical Learning ◽  
Language Processing ◽  
Network Architecture ◽  
State Of The Art ◽  
Rapid Development ◽  
Word Embedding ◽  
Social Networking Services ◽  
Memory Network ◽  
End To End ◽  
Embedding Methods

In this study, we investigated the problem of recommending usernames when people attempt to use the ``@'' sign to mention other people in twitter-like social media. With the extremely rapid development of social networking services, this problem has received considerable attention in recent years. Previous methods have studied the problem from different aspects. Because most of Twitter-like microblogging services limit the length of posts, statistical learning methods may be affected by the problems of word sparseness and synonyms. Although recent progress in neural word embedding methods have advanced the state-of-the-art in many natural language processing tasks, the benefits of word embedding have not been taken into consideration for this problem. In this work, we proposed a novel end-to-end memory network architecture to perform this task. We incorporated the interests of users with external memory. A hierarchical attention mechanism was also applied to better consider the interests of users. The experimental results on a dataset we collected from Twitter demonstrated that the proposed method could outperform state-of-the-art approaches.

scholarly journals Prefrontal spatial working memory network predicts animal's decision making in a free choice saccade task

2016 ◽  
Vol 115 (1) ◽  
pp. 127-142 ◽  
Author(s):  
Kei Mochizuki ◽  
Shintaro Funahashi
Keyword(s):  
Decision Making ◽  
Working Memory ◽  
Network Architecture ◽  
Free Choice ◽  
Spatial Information ◽  
Spatial Working Memory ◽  
Visual Response ◽  
Memory Network ◽  
Saccade Direction ◽  
Saccade Task

While neurons in the lateral prefrontal cortex (PFC) encode spatial information during the performance of working memory tasks, they are also known to participate in subjective behavior such as spatial attention and action selection. In the present study, we analyzed the activity of primate PFC neurons during the performance of a free choice memory-guided saccade task in which the monkeys needed to choose a saccade direction by themselves. In trials when the receptive field location was subsequently chosen by the animal, PFC neurons with spatially selective visual response started to show greater activation before cue onset. This result suggests that the fluctuation of firing before cue presentation prematurely biased the representation of a certain spatial location and eventually encouraged the subsequent choice of that location. In addition, modulation of the activity by the animal's choice was observed only in neurons with high sustainability of activation and was also dependent on the spatial configuration of the visual cues. These findings were consistent with known characteristics of PFC neurons in information maintenance in spatial working memory function. These results suggest that precue fluctuation of spatial representation was shared and enhanced through the working memory network in the PFC and could finally influence the animal's free choice of saccade direction. The present study revealed that the PFC plays an important role in decision making in a free choice condition and that the dynamics of decision making are constrained by the network architecture embedded in this cortical area.

scholarly journals Learning Subword Embedding to Improve Uyghur Named-Entity Recognition

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 139 ◽  
Author(s):  
Alimu Saimaiti ◽  
Lulu Wang ◽  
Tuergen Yibulayin
Keyword(s):  
Network Architecture ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Word Sequence ◽  
Morphological Segmentation ◽  
Sequence Method ◽  
Memory Network

Uyghur is a morphologically rich and typical agglutinating language, and morphological segmentation affects the performance of Uyghur named-entity recognition (NER). Common Uyghur NER systems use the word sequence as input and rely heavily on feature engineering. However, semantic information cannot be fully learned and will easily suffer from data sparsity arising from morphological processes when only the word sequence is considered. To solve this problem, we provide a neural network architecture employing subword embedding with character embedding based on a bidirectional long short-term memory network with a conditional random field layer. Our experiments show that subword embedding can effectively enhance the performance of the Uyghur NER, and the proposed method outperforms the model-based word sequence method.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Sign in / Sign up

Export Citation Format

Share Document