Transfer Learning

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

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

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

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.

Learning structure from unlabeled data, such as discovering groups, compressing representations, or modeling data dis...

Training with a small labeled dataset plus a larger unlabeled dataset, leveraging assumptions like smoothness/cluster...

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

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