Seonglae ChoDropout Rate
Typical dropout rates range from 0.2 to 0.5. During training, dropout randomly masks neurons, creating an ensemble effect similar to AI Ensemble of sub-networks like MoE. This prevents the model from relying too heavily on specific neurons or neuron combinations, functioning as a Model Regularization technique.
For pretraining, a dropout rate of 0 is generally recommended, while for finetuning, rates of 0.1 or higher should be considered.
How Dropout Works
- Forces the network to operate with fewer neurons (e.g., 3 neurons doing the work of 5), making the learning task more challenging and improving training efficiency
- From a Mechanistic interpretability perspective, dropout enforces better allocation of AI Feature Dimensionality, enabling more efficient learning
- The
inplaceparameter modifies input data directly, generating output without additional memory allocation
Dropout Variants