Results for "regularization"

Measure of vector magnitude; used in regularization and optimization.

Techniques that discourage overly complex solutions to improve generalization (reduce overfitting).

Autoencoder using probabilistic latent variables and KL regularization.

Training with a small labeled dataset plus a larger unlabeled dataset, leveraging assumptions like smoothness/cluster structure.

Configuration choices not learned directly (or not typically learned) that govern training or architecture.

A scalar measure optimized during training, typically expected loss over data, sometimes with regularization terms.

Minimizing average loss on training data; can overfit when data is limited or biased.

When a model fits noise/idiosyncrasies of training data and performs poorly on unseen data.

How well a model performs on new data drawn from the same (or similar) distribution as training.

Halting training when validation performance stops improving to reduce overfitting.

Randomly zeroing activations during training to reduce co-adaptation and overfitting.

Expanding training data via transformations (flips, noise, paraphrases) to improve robustness.

A narrow minimum often associated with poorer generalization.

The range of functions a model can represent.

A conceptual framework describing error as the sum of systematic error (bias) and sensitivity to data (variance).