Image Classification Using Generative Neuro Evolution for Deep Learning

By Phillip Verbancsics, Josh Harguess. 2015

48 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tal Ben-Nun
2. Masanori Suganuma
3. Torsten Hoefler
4. Steven R. Young
5. Shinichi Shirakawa
6. T. Karnowski
7. Derek C. Rose
8. Tomoharu Nagao

In Computational Theory and Mathematics

1. Tal Ben-Nun
2. Torsten Hoefler
3. Shinichi Shirakawa
4. Tomoharu Nagao
5. Masanori Suganuma

Citations in the corpus

• A genetic programming approach to designing convolutional neural network architectures (2017, methodology): “[35, 36] have optimized the weights of arti€cial neural networks and CNN by using the hypercube-based neuroevolution of augmenting topologies (HyperNEAT) [31].”
• Demystifying Parallel and Distributed Deep Learning (2018, methodology): “tionary Algorithms (EA) are advantageous for architecture search, as any function (not necessarily differentiable) can be optimized using these methods. HyperNEAT was the first EA successfully applied[234] to deep learning, used for training weights and DNN architecture at the same time; and CoDeepN”
• Optimizing deep learning hyper-parameters through an evolutionary algorithm (2015, background): “The first focuses on replacing stochastic gradient descent and backpropagation with a GA [12, 13, 15, 16, 20, 21].”