Dynamics of Training

By S. Bös, Manfred Opper. 1996

Subfields: Optimization & Training

How researchers see this paper

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

In Optimization & Training

1. Giulio Biroli
2. Mario Geiger
3. Stefano Spigler
4. Stéphane d'Ascoli
5. Levent Sagun
6. Matthieu Wyart
7. Clément Hongler
8. Franck Gabriel

Citations in the corpus

• Scaling description of generalization with number of parameters in deep learning (2019, background): “In the perceptron, the simplest network without hidden layers, the cusp in the test error at the jamming point is also observed and predicted analytically [50, 51, 52, 53, 54, 55].”
• A jamming transition from under- to over-parametrization affects loss landscape and generalization (2018, background): “(2)As a complementary point, a cusp in generalization has been found in several perceptron problems when the ratio of number of training examples by the number of parameters is tuned [36, 12, 6].”