Results for "tokens set"

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

Set of all actions available to the agent.

Rules and controls around generation (filters, validators, structured outputs) to reduce unsafe or invalid behavior.

Structured graph encoding facts as entity–relation–entity triples.

Models effects of interventions (do(X=x)).

Methods that learn training procedures or initializations so models can adapt quickly to new tasks with little data.

A measurable property or attribute used as model input (raw or engineered), such as age, pixel intensity, or token ID.

A continuous vector encoding of an item (word, image, user) such that semantic similarity corresponds to geometric closeness.

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.

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

Popular optimizer combining momentum and per-parameter adaptive step sizes via first/second moment estimates.

Halting training when validation performance stops improving to reduce overfitting.

Methods to set starting weights to preserve signal/gradient scales across layers.

Networks using convolution operations with weight sharing and locality, effective for images and signals.

Achieving task performance by providing a small number of examples inside the prompt without weight updates.

Constraining outputs to retrieved or provided sources, often with citation, to improve factual reliability.

Practices for operationalizing ML: versioning, CI/CD, monitoring, retraining, and reliable production management.

Techniques that fine-tune small additional components rather than all weights to reduce compute and storage.

Removing weights or neurons to shrink models and improve efficiency; can be structured or unstructured.

System for running consistent evaluations across tasks, versions, prompts, and model settings.

Reconstructing a model or its capabilities via API queries or leaked artifacts.

Constraining model outputs into a schema used to call external APIs/tools safely and deterministically.

Measures a model’s ability to fit random noise; used to bound generalization error.

Neural networks can approximate any continuous function under certain conditions.

Estimating parameters by maximizing likelihood of observed data.

Using same parameters across different parts of a model.

Built-in assumptions guiding learning efficiency and generalization.

All possible configurations an agent may encounter.

Recovering training data from gradients.