4 papers
It's Not What Machines Can Learn, It's What We Cannot Teach
Gal Yehuda, Moshe Gabel, Assaf Schuster
Can deep neural networks learn to solve any task, and in particular problems of high complexity? This question attracts a lot of interest, with recent works tackling computationall…
Adaptive Communication Bounds for Distributed Online Learning
Michael Kamp, Mario Boley, Michael Mock +3
We consider distributed online learning protocols that control the exchange of information between local learners in a round-based learning scenario. The learning performance of su…
Gap Aware Mitigation of Gradient Staleness
Saar Barkai, Ido Hakimi, Assaf Schuster
Cloud computing is becoming increasingly popular as a platform for distributed training of deep neural networks. Synchronous stochastic gradient descent (SSGD) suffers from substan…
Efficient Multi-Resource, Multi-Unit VCG Auction
Liran Funaro, Orna Agmon Ben-Yehuda, Assaf Schuster
We consider the optimization problem of a multi-resource, multi-unit VCG auction that produces an optimal, i.e., non-approximated, social welfare. We present an algorithm that solv…