3 citations · 3 across the 3 of their papers we have counts for
4 papers
Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation
Anastasis Kratsios, Simone Brugiapaglia, Bum Jun Kim +2
Feedforward neural network (NN) expressivity is typically studied by emulating optimal basis-expansion schemes. While powerful, this perspective is incomplete: it primarily capture…
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…
Classifying spaces and the Lascar group
Tim Campion, Greg Cousins, Jinhe Ye
We show that the Lascar group of a first order theory is naturally isomorphic to the fundamental group of the classifyi…
Embedded Picard-Vessiot extensions
Quentin Brouette, Greg Cousins, Anand Pillay +1
We prove that if T is a theory of large, bounded, fields of characteristic zero, with almost quantifier elimination, and T_D is the model companion of T + "D is a derivation", then…