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0111 · Jan 202319922001200920172026
2 results for Min-degree-interpolator

The paper explores how different network architectures learn logical functions under GOTU, finding that a min-degree-interpolator is learned.

problem Learning logical functions with a focus on generalization on the unseen.
method Study of different network architectures trained by SGD under GOTU.
result For sparse functions and certain network models, a min-degree-interpolator is learned on the unseen.

Study shows Transformers can generalize to varying task lengths.

problem Understanding when and how Transformers can generalize to different input lengths.
method Proposed a unifying framework and introduced the RASP-Generalization Conjecture.
result Transformers tend to length generalize on tasks if solvable by short RASP programs.