References of "IEEE Transactions on Neural Networks and Learning Systems"
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See detailWhy is Everyone Training Very Deep Neural Network with Skip Connections?
Oyedotun, Oyebade UL; Al Ismaeil, Kassem; Aouada, Djamila UL

in IEEE Transactions on Neural Networks and Learning Systems (2021)

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See detailAutomatic Analysis of Facial Expressions Based on Deep Covariance Trajectories
Otberdout, Naima; Kacem, Anis UL; Daoudi, Mohamed et al

in IEEE Transactions on Neural Networks and Learning Systems (2019)

In this article, we propose a new approach for facial expression recognition (FER) using deep covariance descriptors. The solution is based on the idea of encoding local and global deep convolutional ... [more ▼]

In this article, we propose a new approach for facial expression recognition (FER) using deep covariance descriptors. The solution is based on the idea of encoding local and global deep convolutional neural network (DCNN) features extracted from still images, in compact local and global covariance descriptors. The space geometry of the covariance matrices is that of symmetric positive definite (SPD) matrices. By conducting the classification of static facial expressions using a support vector machine (SVM) with a valid Gaussian kernel on the SPD manifold, we show that deep covariance descriptors are more effective than the standard classification with fully connected layers and softmax. Besides, we propose a completely new and original solution to model the temporal dynamic of facial expressions as deep trajectories on the SPD manifold. As an extension of the classification pipeline of covariance descriptors, we apply SVM with valid positive definite kernels derived from global alignment for deep covariance trajectories classification. By performing extensive experiments on the Oulu-CASIA, CK+, static facial expression in the wild (SFEW), and acted facial expressions in the wild (AFEW) data sets, we show that both the proposed static and dynamic approaches achieve the state-of-the-art performance for FER outperforming many recent approaches. [less ▲]

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See detailPrototype Incorporated Emotional Neural Network (PI-EmNN)
Oyedotun, Oyebade UL; Khashman, Adnan

in IEEE Transactions on Neural Networks and Learning Systems (2017)

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ‘engineering’ prospects in ANN have relied on motivations from cognition and psychology studies. So ... [more ▼]

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ‘engineering’ prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, prototype learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype and adaptive learning theories. We refer to our new model as “PI-EmNN” (Prototype-Incorporated Emotional Neural Network). Furthermore, we apply the proposed model to two real-life challenging tasks, namely; static hand gesture recognition and face recognition, and compare the result to those obtained using the popular back propagation neural network (BPNN), emotional back propagation neural network (EmNN), deep networks and an exemplar classification model, k-nearest neighbor (k-NN). [less ▲]

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See detailOn Learning Navigation Behaviors for Small Mobile Robots With Reservoir Computing Architectures
Antonelo, Eric Aislan UL; Schrauwen, B.

in IEEE Transactions on Neural Networks and Learning Systems (2015), 26(4), 763-780

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