The curse of overparametrization in adversarial training: Precise analysis of robust
generalization for random features regressi Page 1 The Annals of Statistics 2024, Vol. 52, No. 2 …

We consider the Ising perceptron model with N spins and M= N* alpha patterns, with a
general activation function U that is bounded above. For U bounded away from zero, or U a …

We study a family of Ising perceptron models with {0, 1}-valued activation functions. This
includes the classical half-space models, as well as some of the symmetric models …

We consider the spherical perceptron with Gaussian disorder. This is the set S of points σ∈
RN on the sphere of radius N satisfying⟨ ga, σ⟩≥ κ N for all 1≤ a≤ M, where (ga) a= 1 M …

The binary perceptron problem asks us to find a sign vector in the intersection of
independently chosen random halfspaces with intercept $-\kappa $. We analyze the …

We study the binary and continuous negative-margin perceptrons as simple nonconvex
neural network models learning random rules and associations. We analyze the geometry of …

We consider a recently proposed model to understand the rigidity transition in confluent
tissues and we derive the dynamical mean field theory (DMFT) equations that describes …

While classical in many theoretical settings—and in particular in statistical physics-inspired
works—the assumption of Gaussian iid input data is often perceived as a strong limitation in …

When can the input of a ReLU neural network be inferred from its output? In other words,
when is the network injective? We consider a single layer, x↦ ReLU (W x), with a random …

The existence of" lottery tickets" arxiv: 1803.03635 at or near initialization raises the
tantalizing question of whether large models are necessary in deep learning, or whether …