Nettsider med emneord «High-performance computing»

In this project, we modify adaptive variants of unbiased quantization schemes tailored to general variational inequality (VI) problems including those with convex-like structures, e.g., convex minimization, saddle-point problems, and games [4–6] with several applications such as auction theory [7], multi-agent and robust reinforcement learning (RL) [8], adversarially robust learning [9], and generative adversarial networks. In particular, our goal is to design novel adaptive and layer-wise compression schemes tailored to tasks beyond supervised learning building on our recent work on SOTA compression schemes for deep learning [1,10].