Multitask Learning

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.

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

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.

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