4 citations · 4 across the 3 of their papers we have counts for
3 papers
Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity
Anastasis Kratsios, Gregory Cousins, Haitz Sáez de Ocáriz Borde +2
We show that, in a precise sense, a broad class of feedforward neural networks learn (have finite sample complexity) in the PAC model: every fixed finite feedforward architecture w…
Invariance, encodings, and generalization: learning identity effects with neural networks
S. Brugiapaglia, M. Liu, P. Tupper
Often in language and other areas of cognition, whether two components of an object are identical or not determines if it is well formed. We call such constraints identity effects.…
Generalizing Outside the Training Set: When Can Neural Networks Learn Identity Effects?
Simone Brugiapaglia, Matthew Liu, Paul Tupper
Often in language and other areas of cognition, whether two components of an object are identical or not determine whether it is well formed. We call such constraints identity effe…