How Saudi Arabia's spending spree reached the end of the line Autocratic monarchs once left an echo of their glory in the ruins of the megaprojects they commanded at the peak of their unchallenged power. Those monumental physical traces are to be found in the fertile plains, mountainsides and deserts of the Middle East. But one of their most prominent modern counterparts may only have a digital footprint to leave behind for some of his most ambitious concepts. A decade ago, the Crown Prince of Saudi Arabia Mohammed bin Salman - or MBS as he is widely known - decreed a revisioning of his country that leapt from the realm of science fiction. It was called Vision 2030. Extraordinary monolithic structures were to help bring forth new technological marvels not just for the Kingdom but for the world.

We consider nonconvex stochastic optimization problems in the asynchronous centralized distributed setup where the communication times from workers to a server can not be ignored, and the computation and communication times are potentially different for all workers.

These methods can be broadly categorized into two types: function learning and operator learning approaches. In function learning, the goal is to directly learn the solution.

Effective communication between the server and workers plays a key role in distributed optimization. In this paper, we focus on optimizing communication, uncovering inefficiencies in prevalent downlink compression approaches.

(Theorem 4). We also consider Assumption 3. Note that this assumption does not restrict the class of considered Iterative algorithms are traditionally evaluated based on their gradient complexity .

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/