Neuroevolution in deep neural networks

2021 ◽  
Vol 14 (1) ◽  
pp. 3-7
Author(s):  
Edgar Galván
Keyword(s):  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Crucial Role ◽  
Deep Neural Networks ◽  
Feasible Method ◽  
Full Version

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNNs). These methods play a crucial role in the success or failure of the DNNs for most problems. Evolutionary Algorithms are gaining momentum as a computationally feasible method for the automated optimisation of DNNs. Neuroevolution is a term that describes these processes. This newsletter article summarises the full version available at https://arxiv.org/abs/2006.05415.

scholarly journals Evolutionary Algorithms in Approximate Computing: A Survey

2021 ◽  
Vol 16 (2) ◽  
pp. 1-12
Author(s):  
Lukas Sekanina
Keyword(s):  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Design Automation ◽  
Deep Neural Networks ◽  
Approximation Methods ◽  
Approximate Computing ◽  
System Parameters ◽  
Neural Architecture ◽  
Trade Offs

In recent years, many design automation methods have been developed to routinely create approximate implementations of circuits and programs that show excellent trade-offs between the quality of output and required resources. This paper deals with evolutionary approximation as one of the popular approximation methods. The paper provides the first survey of evolutionary algorithm (EA)-based approaches applied in the context of approximate computing. The survey reveals that EAs are primarily applied as multi-objective optimizers. We propose to divide these approaches into two main classes: (i) parameter optimization in which the EA optimizes a vector of system parameters, and (ii) synthesis and optimization in which EA is responsible for determining the architecture and parameters of the resulting system. The evolutionary approximation has been applied at all levels of design abstraction and in many different applications. The neural architecture search enabling the automated hardware-aware design of approximate deep neural networks was identified as a newly emerging topic in this area.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks
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