The Loss Surfaces of Multilayer Networks¶
Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B., & LeCun, Y. (2015). The Loss Surfaces of Multilayer Networks. Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS), 192-204.
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Primes¶
- Multi Path Convergence
- In optimization and machine learning, gradient descent from many random initializations on a non-convex landscape converges to the same basin, and the empirical fact that different seeds yield qualitatively similar trained models is multi-path convergence.
This sourceAnalyzes the loss landscape of deep networks, showing that gradient descent from many random initializations converges to qualitatively similar low-loss minima.
- In optimization and machine learning, gradient descent from many random initializations on a non-convex landscape converges to the same basin, and the empirical fact that different seeds yield qualitatively similar trained models is multi-path convergence.
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