Dropout: A Simple Way to Prevent Neural Networks from Overfitting.¶
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929-1958.
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Primes¶
- Feedforward Inhibition
- In machine-learning architectures gating mechanisms, attention-with-temperature, dropout, weight decay, and load-balanced expert routing all implement a "let through but bounded" shape.
This sourceRepresentative of ML 'let through but bounded' mechanisms (dropout, alongside weight decay, gating, temperature) that pair activation with a built-in constraint.
- In machine-learning architectures gating mechanisms, attention-with-temperature, dropout, weight decay, and load-balanced expert routing all implement a "let through but bounded" shape.
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