Popular models for predicting the output (or label, response, outcome)yfrom theinput (orcovariate)xhavebeenfound erroneous when confronted with a distribution change, even from an essentially irrelevant perturbation like a position shift or background change forimages [91,6,102,41,2,27].

In this paper we study in detail the training dynamics of a simple type of neural network: a single hidden layer trained to performaclassificationtask.

The design automation of analog circuits poses significant challenges in terms of the large design space, complex interdependencies between circuit specifications, and resource-intensive simulations.

The study of provable adversarial robustness has mostly been limited to classification tasks and models with one-dimensional real-valued outputs.

Taking advantage of an analogy between Transformer stages and the evolution of a dynamical system of multiple interacting particles, we formulate a temporal evolution scheme,TransEvolve, to bypass costly dot-product attention over multiple stacked layers.