Results for "continual learning"

Humans assist or override autonomous behavior.

Robots learning via exploration and growth.

Inferring and aligning with human preferences.

A subfield of AI where models learn patterns from data to make predictions or decisions, improving with experience rather than explicit rule-coding.

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

A structured collection of examples used to train/evaluate models; quality, bias, and coverage often dominate outcomes.

Designing input features to expose useful structure (e.g., ratios, lags, aggregations), often crucial outside deep learning.

Human or automated process of assigning targets; quality, consistency, and guidelines matter heavily.

System design where humans validate or guide model outputs, especially for high-stakes decisions.

Strategy mapping states to actions.

Systematic error introduced by simplifying assumptions in a learning algorithm.

Combines value estimation (critic) with policy learning (actor).

Balancing learning new behaviors vs exploiting known rewards.

Continuous cycle of observation, reasoning, action, and feedback.

Combining simulation and real-world data.

RL without explicit dynamics model.

Learned model of environment dynamics.

Learning without catastrophic forgetting.

Deep learning system for protein structure prediction.

AI limited to specific domains.

A measure of a model class’s expressive capacity based on its ability to shatter datasets.

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

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

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

Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.

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

Hidden behavior activated by specific triggers, causing targeted mispredictions or undesired outputs.

Reduction in uncertainty achieved by observing a variable; used in decision trees and active learning.

All possible configurations an agent may encounter.

Models evaluating and improving their own outputs.