@article {4468, title = {Can we Contain Covid-19 without Locking-down the Economy?}, year = {2020}, month = {03/2020}, abstract = {

We present an analysis of a risk-based selective quarantine model where the population is divided into low and high-risk groups. The high-risk group is quarantined until the low-risk group achieves herd-immunity. We tackle the question of whether this model is safe, in the sense that the health system can contain the number of low-risk people that require severe ICU care (such as life support systems).

}, author = {Shai Shalev-Shwartz and Amnon Shashua} } @article {4482, title = {An Exit Strategy from the Covid-19 Lockdown based on Risk-sensitive Resource Allocation}, year = {2020}, month = {04/2020}, abstract = {

We propose an exit strategy from the COVID-19 lockdown, which is based on a risk-sensitive levels of social distancing. At the heart of our approach is the realization that the most effective, yet limited in number, resources should protect those at high risk rather than applied uniformly across the population. By generalizing the SEIR model to mixed populations, and based on existing data in Israel, we present an analysis of the maximal load on the health system and the total mortality. We argue that risk-sensitive resource allocation combined with risk-sensitive levels of social distancing enables to lower the overall mortality toll in parallel to resumption of economic activity.

}, author = {Shai Shalev-Shwartz and Amnon Shashua} } @article {1439, title = {Notes on Hierarchical Splines, DCLNs and i-theory}, year = {2015}, abstract = {

We define an extension of classical additive splines for multivariate
function approximation that we call hierarchical splines. We show that the
case of hierarchical, additive, piece-wise linear splines includes present-day
Deep Convolutional Learning Networks (DCLNs) with linear rectifiers and
pooling (sum or max). We discuss how these observations together with
i-theory may provide a framework for a general theory of deep networks.

}, author = {Tomaso Poggio and Lorenzo Rosasco and Amnon Shashua and Nadav Cohen and F. Anselmi} }