The evolution of a generalized neural learning rule

By Jeff Orchard, L. Wang. 2016

19 citations recorded by the source corpus.

Subfields: Artificial Intelligence

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. Quoc V. Le
2. Barret Zoph
3. Irwan Bello
4. Frank Hutter
5. Chen Liang
6. David R. So
7. Mohit Bansal
8. Vijay Vasudevan

Citations in the corpus

• Neural Optimizer Search with Reinforcement Learning (2017, methodology): “For example, the neural networks used for experiments in (Orchard & Wang, 2016) have around 100 weights, which is quite small compared to today’s standards.”
• AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (2020, background): “tures, such as a second neural network [73, 60], an LSTM [67], a hierarchical RNN [88], or even a different LSTM for each weight [53].”
• Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “ta-Learning Evolutionary Artificial Neural Networks (MLEANN) in which evolution searches for initial weights, neural architectures and transfer functions, and finally optimizes existing learning rules. Orchard and Wang (2016) encode the learning rule itself as a network that is then evolved. One gener”