High Efficiency
High efficiency in various computational domains is a central research theme, aiming to minimize resource consumption (time, memory, energy) while maintaining or improving performance. Current efforts focus on developing novel algorithms and architectures, such as optimized Thompson sampling for reinforcement learning, sparse attention mechanisms for transformers, and efficient model compression techniques, to achieve this goal across diverse applications including natural language processing, computer vision, and robotics. These advancements are crucial for deploying complex AI models on resource-constrained devices and for accelerating scientific discovery in data-intensive fields.
Papers
DDTSE: Discriminative Diffusion Model for Target Speech Extraction
Leying Zhang, Yao Qian, Linfeng Yu, Heming Wang, Hemin Yang, Long Zhou, Shujie Liu, Yanmin Qian
Efficient RRT*-based Safety-Constrained Motion Planning for Continuum Robots in Dynamic Environments
Peiyu Luo, Shilong Yao, Yiyao Yue, Jiankun Wang, Hong Yan, Max Q. -H. Meng