Results for "noisy gradients"

Inferring the agent’s internal state from noisy sensor data.

Uses an exponential moving average of gradients to speed convergence and reduce oscillation.

Gradients shrink through layers, slowing learning in early layers; mitigated by ReLU, residuals, normalization.

Gradients grow too large, causing divergence; mitigated by clipping, normalization, careful init.

An RNN variant using gates to mitigate vanishing gradients and capture longer context.

Limiting gradient magnitude to prevent exploding gradients.

Allows gradients to bypass layers, enabling very deep networks.

Recovering training data from gradients.