Results for "data → model"

Storing results to reduce compute.

Software pipeline converting raw sensor data into structured representations.

Models estimating recidivism risk.

Learning only from current policy’s data.

Probabilistic model for sequential data with latent states.

Task instruction without examples.

Requirement to reveal AI usage in legal decisions.

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

A high-capacity language model trained on massive corpora, exhibiting broad generalization and emergent behaviors.

Attacks that manipulate model instructions (especially via retrieved content) to override system goals or exfiltrate data.

Observing model inputs/outputs, latency, cost, and quality over time to catch regressions and drift.

Capabilities that appear only beyond certain model sizes.

Classical statistical time-series model.

Model execution path in production.

Models that process or generate multiple modalities, enabling vision-language tasks, speech, video understanding, etc.

Using same parameters across different parts of a model.

A single attention mechanism within multi-head attention.

Probabilistic graphical model for structured prediction.

Cost to run models in production.

Local surrogate explanation method approximating model behavior near a specific input.

Standardized documentation describing intended use, performance, limitations, data, and ethical considerations.

Required descriptions of model behavior and limits.

Learning a function from input-output pairs (labeled data), optimizing performance on predicting outputs for unseen inputs.

Reusing knowledge from a source task/domain to improve learning on a target task/domain, typically via pretrained models.

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

One complete traversal of the training dataset during training.

Converting text into discrete units (tokens) for modeling; subword tokenizers balance vocabulary size and coverage.

Letting an LLM call external functions/APIs to fetch data, compute, or take actions, improving reliability.

Probabilistic energy-based neural network with hidden variables.

Maintaining alignment under new conditions.