scholarly journals Calibrating the Classifier: Siamese Neural Network Architecture for End-to-End Arousal Recognition from ECG

2019 ◽  
pp. 1-13 ◽  
Author(s):  
Andrea Patanè ◽  
Marta Kwiatkowska
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
End To End

scholarly journals End-to-end neural network architecture for fraud scoring in card payments

2018 ◽  
Vol 105 ◽  
pp. 175-181 ◽  
Author(s):  
Jon Ander Gómez ◽  
Juan Arévalo ◽  
Roberto Paredes ◽  
Jordi Nin
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
End To End

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.

Evolving Neural Network Architecture

1993 ◽  
Author(s):  
John R. McDonnell ◽  
Don Waagen
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Neural Network Architecture

scholarly journals Exploring a Siamese Neural Network Architecture for One-Shot Drug Discovery

2020 ◽  
Author(s):  
Luis Torres ◽  
Nelson Monteiro ◽  
Jose Oliveira ◽  
Joel Arrais ◽  
Bernardete Ribeiro
Keyword(s):  
Neural Network ◽  
Drug Discovery ◽  
Network Architecture ◽  
Neural Network Architecture

Deepening the IDA* algorithm for knowledge graph reasoning through neural network architecture

2021 ◽  
Vol 429 ◽  
pp. 101-109
Author(s):  
Qi Wang ◽  
Yongsheng Hao ◽  
Feng Chen
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Knowledge Graph ◽  
Neural Network Architecture

scholarly journals Learning Subject-Generalized Topographical EEG Embeddings Using Deep Variational Autoencoders and Domain-Adversarial Regularization

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1792
Author(s):  
Juan Hagad ◽  
Tsukasa Kimura ◽  
Ken-ichi Fukui ◽  
Masayuki Numao
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Emotion Classification ◽  
Limited Data ◽  
Neural Network Architecture ◽  
Building Models ◽  
The Subject ◽  
Data Constraints ◽  
Input Level ◽  
Normally Distributed

Two of the biggest challenges in building models for detecting emotions from electroencephalography (EEG) devices are the relatively small amount of labeled samples and the strong variability of signal feature distributions between different subjects. In this study, we propose a context-generalized model that tackles the data constraints and subject variability simultaneously using a deep neural network architecture optimized for normally distributed subject-independent feature embeddings. Variational autoencoders (VAEs) at the input level allow the lower feature layers of the model to be trained on both labeled and unlabeled samples, maximizing the use of the limited data resources. Meanwhile, variational regularization encourages the model to learn Gaussian-distributed feature embeddings, resulting in robustness to small dataset imbalances. Subject-adversarial regularization applied to the bi-lateral features further enforces subject-independence on the final feature embedding used for emotion classification. The results from subject-independent performance experiments on the SEED and DEAP EEG-emotion datasets show that our model generalizes better across subjects than other state-of-the-art feature embeddings when paired with deep learning classifiers. Furthermore, qualitative analysis of the embedding space reveals that our proposed subject-invariant bi-lateral variational domain adversarial neural network (BiVDANN) architecture may improve the subject-independent performance by discovering normally distributed features.

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