2018

SEGEN: Sample-Ensemble Genetic Evolutional Network Model

Zhang, Jiawei, Cui, Limeng, Gouza, Fisher B.

Understand

Deep learning, a rebranding of deep neural network research works, has achieved a remarkable success in recent years.

With multiple hidden layers, deep learning models aim at computing the hierarchical feature representations of the observational data.
Meanwhile, due to its severe disadvantages in data consumption, computational resources, parameter tuning costs and the lack of result explainability, deep learning has also suffered from lots of criticism.
In this paper, we will introduce a new representation learning model, namely "Sample-Ensemble Genetic Evolutionary Network" (SEGEN), which can serve as an alternative approach to deep learning models.

Built on

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