Meta-Learning

A subfield of AI where models learn patterns from data to make predictions or decisions, improving with experience ra...

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

Automatically learning useful internal features (latent variables) that capture salient structure for downstream tasks.

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

When some classes are rare, requiring reweighting, resampling, or specialized metrics.

Learning from data by constructing “pseudo-labels” (e.g., next-token prediction, masked modeling) without manual ...

Learning where data arrives sequentially and the model updates continuously, often under changing distributions.

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

Training one model on multiple tasks simultaneously to improve generalization through shared structure.