Public cloud GPU clusters are becoming emerging platforms for training distributed deep
learning jobs. Under this training paradigm, the job scheduler is a crucial component to …

Adversarial distillation (AD) is a highly effective method for enhancing the robustness of
small models. Contrary to expectations a high-performing teacher model does not always …

Existing adversarial example defense methods are static, meaning they remain unchanged
once training is completed, regardless of how attack methods change. Consequently, static …

Sharding provides an opportunity to overcome the inherent scalability challenges of the
blockchain, which is the infrastructure for the next generation of the Web. In a sharding …

In large-scale, distributed high-performance computing systems, the increasing complexity
of job scheduling has expanded along with the growth of computational resources and job …

Missing values and unreleased figures are common but highly important for backtesting and
real-time analysis in the financial industry, yet underexploited in the existing literature. In this …

Deep Reinforcement Learning (DRL) achieves great success in various domains.
Communication in today's DRL algorithms takes non-negligible time compared to the …