Results for "PAC"
Latent Space
IntermediateThe internal space where learned representations live; operations here often correlate with semantics or generative factors.
Latent space is like a hidden room in a house where all the important features of the house are stored, but you can't see them directly. Imagine if you could take a picture of a house and then represent its features—like the number of rooms, the size of the yard, and the color of the walls—using ...
A model is PAC-learnable if it can, with high probability, learn an approximately correct hypothesis from finite samples.
A measure of a model class’s expressive capacity based on its ability to shatter datasets.
A theoretical framework analyzing what classes of functions can be learned, how efficiently, and with what guarantees.