Scaling and Generalization in Neural Networks: A Case Study

By Subutai Ahmad, G. Tesauro. 1988

Subfields: Scaling & Learning Theory

How researchers see this paper

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

In Scaling & Learning Theory

1. Jaehoon Lee
2. Yasaman Bahri
3. Jared Kaplan
4. Jared Kaplan
5. Ethan Dyer
6. Behrooz Ghorbani
7. Taskin Kavzoglu
8. Paul M. Mather

Citations in the corpus

• Scaling Laws for Neural Machine Translation (2021, background): “power(x_d , p[2]) + p[3] - y def fit_model(x, y, f_scale): X = x.”
• Explaining neural scaling laws (2021, background): “These rely on the idea that additional data (in the infinite model-size limit) or 6.”
• The use of backpropagating artificial neural networks in land cover classification (2003, result): “Similar results are also reported by Ahmad and Tesauro ( 1989 ) who find that, for a fixed network size, the failure rate decreases exponentially with the size of the training set.”