Poker has been a challenge problem in game theory, operations research, and artificial intelligence for decades. As a game of imperfect information, it involves obstacles not present in games like chess and go, and requires…
We introduce a fully online model of maximum cardinality matching in which all vertices arrive online. On the arrival of a vertex, its incident edges to previously-arrived vertices are revealed. Each vertex has a deadline…
Stochastic Gradient Descent or SGD is the most popular algorithm for large-scale optimization. In SGD, the gradient is estimated by uniform sampling with sample size one. There have been several results that show better gradient…
Computation, unlike mathematics, is a physical process that takes time, energy, and space. Humans have dominated this planet’s ecosystem by learning to share and consolidate the outcome of their computation in an unprecedented way. Now…
We show that the perfect matching problem in general graphs is in Quasi-NC. That is, we give a deterministic parallel algorithm which runs in polylogarithmic time on quasi-polynomially many processors. The result is obtained by…
Wolong: A Backend Optimizer for Deep Learning Computation and Open PAI Chair: Jilong Xue This talk presents Wolong, a backend optimization system for accelerating deep learning computation on graphics processing units (GPUs). It automatically applies…
In an ideal world, there would be infinite computing resources. These resources would be free and sustainable, with no impact on the future of our planet. They would run services that would be accessible anytime…
