This paper proposes an overview of the Neural Nets optimization techniques. A comparison and discussion is also given for CNN architectures optimization for ...
This paper proposes an overview of the Neural Nets optimization techniques. A comparison and discussion is also given for CNN architectures optimization for ...
This paper proposes an overview of the Neural Nets optimization techniques. A comparison and discussion is also given for CNN architectures optimization for.
We present an adaptive regularization algorithm that can be effectively applied to the optimization problem in deep learning framework. Our regularization ...
We introduce IntelliSwAS approach for optimizing deep neural network architectures for a classification or regression task.
Aug 17, 2018 · This paper proposes a new methodology which can work on the estimation of hidden layers and their respective neurons for FNN.
Optimizing Deep Neural Network Architectures: an overview. Bouzar-Benlabiod, L., Rubin, S. H., & Benaida, A. In IRI, pages 25-32, 2021. IEEE.
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Abstract. Optimization is a critical component in deep learning. We think optimization for neural net- works is an interesting topic for theoretical ...
It is a novel CNN optimization and construction method based on pruning designed to establish the importance of convolutional layers.
This paper proposes a new methodology which combines the advantages of Tabu search and Gradient descent with momentum backpropagation training algorithm [6] for ...