How can signal vs. noise be reduced in a neural network during training?

Reducing signal versus noise in a neural network during training is crucial for improving model generalization and performance. The option regarding non-saturating, nonlinear activation functions is correct because these types of functions help maintain gradients effectively throughout the training process.

When using non-saturating activation functions like ReLU (Rectified Linear Unit) or its variants, the gradients do not diminish as the input increases. This helps mitigate problems related to vanishing gradients, which can occur with saturating functions such as sigmoid or tanh. When saturation occurs, the gradients become very small, making it difficult for the network to learn relevant features from the training data—this means the model may not effectively differentiate between meaningful signals and random noise.

In contrast, the other options do not specifically address the balance between signal and noise. Increasing the number of training epochs can lead to overfitting, where the model learns noise rather than the underlying patterns in the data. Adjusting learning rates can help with convergence speed but does not inherently improve the model's ability to distinguish between signal and noise. While learning rates can affect training dynamics, they do not directly tackle issues related to saturation and gradient flow like non-saturating activation functions do.