Results for "“learning to learn”"

Learning action mapping directly from demonstrations.

Learning by minimizing prediction error.

Robots learning via exploration and growth.

Deep learning system for protein structure prediction.

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

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

Controls the size of parameter updates; too high diverges, too low trains slowly or gets stuck.

Achieving task performance by providing a small number of examples inside the prompt without weight updates.

Selecting the most informative samples to label (e.g., uncertainty sampling) to reduce labeling cost.

Ordering training samples from easier to harder to improve convergence or generalization.

Training across many devices/silos without centralizing raw data; aggregates updates, not data.

A theoretical framework analyzing what classes of functions can be learned, how efficiently, and with what guarantees.

Inferring reward function from observed behavior.

Inferring and aligning with human preferences.