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Activation function

In computational networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard computer… Expand
Wikipedia

Related topics

Related topics

31 relations
Action potentialArtificial neural networkArtificial neuronAutoencoder
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2019
Review
2019
A Survey on Neural Architecture Search
The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide… Expand
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Review
2019
Review
2019
Understanding Neural Networks via Feature Visualization: A survey
A neuroscience method to understanding the brain is to find and study the preferred stimuli that highly activate an individual… Expand
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Review
2019
Review
2019
Designing neural networks through neuroevolution
Much of recent machine learning has focused on deep learning, in which neural network weights are trained through variants of… Expand
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Review
2018
Review
2018
Activation Functions: Comparison of trends in Practice and Research for Deep Learning
Deep neural networks have been successfully used in diverse emerging domains to solve real world complex problems with may more… Expand
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Highly Cited
2018
Highly Cited
2018
Searching for Activation Functions
The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance… Expand
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Review
2017
Review
2017
Low-cost adsorbents derived from agricultural by-products/wastes for enhancing contaminant uptakes from wastewater: A Review
A major threat to the comfort of human life has been imposed by increased industrialization and urbanization. The generation and… Expand
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Highly Cited
2017
Highly Cited
2017
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One… Expand
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Highly Cited
2011
Highly Cited
2011
Deep Sparse Rectifier Neural Networks
While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent neurons, the latter work better for… Expand
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Highly Cited
1998
Highly Cited
1998
The Atomic Components of Thought
Contents: Preface. J.R. Anderson, C. Lebiere, Introduction. J.R. Anderson, C. Lebiere, Knowledge Representation. J.R. Anderson, C… Expand
Highly Cited
1993
Highly Cited
1993
Multilayer Feedforward Networks with a Non-Polynomial Activation Function Can Approximate Any Function
Several researchers characterized the activation functions under which multilayer feedforwardnetworks can act as universal… Expand
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