Results for "probabilistic loss"

Penalizes confident wrong predictions heavily; standard for classification and language modeling.

The shape of the loss function over parameter space.

Autoencoder using probabilistic latent variables and KL regularization.

A function measuring prediction error (and sometimes calibration), guiding gradient-based optimization.

A proper scoring rule measuring squared error of predicted probabilities for binary outcomes.

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

Iterative method that updates parameters in the direction of negative gradient to minimize loss.

Probabilistic energy-based neural network with hidden variables.

Probabilistic graphical model for structured prediction.

Diffusion model trained to remove noise step by step.

The learned numeric values of a model adjusted during training to minimize a loss function.

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

Assigning labels per pixel (semantic) or per instance (instance segmentation) to map object boundaries.

Visualization of optimization landscape.

Lowest possible loss.

Maximum expected loss under normal conditions.

The degree to which predicted probabilities match true frequencies (e.g., 0.8 means ~80% correct).

Systems where failure causes physical harm.

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

Halting training when validation performance stops improving to reduce overfitting.

Measures divergence between true and predicted probability distributions.

A narrow minimum often associated with poorer generalization.

A wide basin often correlated with better generalization.

Matrix of second derivatives describing local curvature of loss.

Two-network setup where generator fools a discriminator.

Pixel-wise classification of image regions.

Minimum relative to nearby points.

Applying learned patterns incorrectly.

Loss of old knowledge when learning new tasks.

Learning policies from expert demonstrations.