Continuously Constructive Deep Neural Networks

Traditionally, deep learning algorithms update the network weights, whereas the network architecture is chosen manually using a process of trial and error. In this paper, we propose two novel approaches that automatically update the network structure while also learning its weights. The novelty of o...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 31; H. 4; S. 1124 - 1133
Hauptverfasser: Irsoy, Ozan, Alpaydin, Ethem
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial neural networks
Complexity theory
Computer architecture
Constructive learning
Deep learning
Learning algorithms
Machine learning
Network architecture
Neural networks
Parameterization
Parameters
Road transportation
Task complexity
Training
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:Traditionally, deep learning algorithms update the network weights, whereas the network architecture is chosen manually using a process of trial and error. In this paper, we propose two novel approaches that automatically update the network structure while also learning its weights. The novelty of our approach lies in our parameterization, where the depth, or additional complexity, is encapsulated continuously in the parameter space through control parameters that add additional complexity. We propose two methods. In tunnel networks, this selection is done at the level of a hidden unit, and in budding perceptrons, this is done at the level of a network layer; updating this control parameter introduces either another hidden unit or layer. We show the effectiveness of our methods on the synthetic two-spiral data and on three real data sets of MNIST, MIRFLICKR, and CIFAR, where we see that our proposed methods, with the same set of hyperparameters, can correctly adjust the network complexity to the task complexity.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2019.2918225