Neural networks handle uncertainty using probabilistic approaches that quantify confidence in predictions. For example, softmax outputs probabilities for classification tasks, indicating the model's confidence in each class. However, these probabilities might not reflect true uncertainty, prompting techniques like temperature scaling or Bayesian neural networks for better calibration.
Dropout, commonly used for regularization during training, can also estimate uncertainty in predictions. By applying dropout at inference time (Monte Carlo Dropout), the network generates multiple outputs for the same input. Variations in these outputs indicate uncertainty, useful in applications like medical diagnostics or autonomous driving where confidence matters.
Additionally, ensemble methods combine predictions from multiple neural networks to improve robustness and measure uncertainty. For instance, an ensemble of models trained on slightly different data subsets can provide a more reliable prediction by averaging their outputs. These approaches are critical for deploying neural networks in real-world scenarios, ensuring safety and reliability even in uncertain conditions.