Results for "probability mapping"

A model that assigns probabilities to sequences of tokens; often trained by next-token prediction.

A measure of randomness or uncertainty in a probability distribution.

Measures divergence between true and predicted probability distributions.

Measures how much information an observable random variable carries about unknown parameters.

Two-network setup where generator fools a discriminator.

Probability of treatment assignment given covariates.

Sample mean converges to expected value.

Sampling from easier distribution with reweighting.

Predicting borrower default risk.

Scalar summary of ROC; measures ranking ability, not calibration.

When information from evaluation data improperly influences training, inflating reported performance.

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

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

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

Training objective where the model predicts the next token given previous tokens (causal modeling).

Predicts masked tokens in a sequence, enabling bidirectional context; often used for embeddings rather than generation.

Model-generated content that is fluent but unsupported by evidence or incorrect; mitigated by grounding and verification.

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

Artificially created data used to train/test models; helpful for privacy and coverage, risky if unrealistic.

Scales logits before sampling; higher increases randomness/diversity, lower increases determinism.

Raw model outputs before converting to probabilities; manipulated during decoding and calibration.

Converts logits to probabilities by exponentiation and normalization; common in classification and LMs.

Exponential of average negative log-likelihood; lower means better predictive fit, not necessarily better utility.

Fundamental recursive relationship defining optimal value functions.

A theoretical framework analyzing what classes of functions can be learned, how efficiently, and with what guarantees.

Probabilistic model for sequential data with latent states.

Formal framework for sequential decision-making under uncertainty.

Models that learn to generate samples resembling training data.

Expected return of taking action in a state.

Learns the score (∇ log p(x)) for generative sampling.