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
COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models
Bowen Shen, Zheng Lin, Yuanxin Liu, Zhengxiao Liu, Lei Wang, Weiping Wang
Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks
Leonie Basso, Zhao Ren, Wolfgang Nejdl
Efficient and Effective Augmentation Strategy for Adversarial Training
Sravanti Addepalli, Samyak Jain, R. Venkatesh Babu
On the Efficiency of Ethics as a Governing Tool for Artificial Intelligence
Nicholas Kluge Corrêa, Nythamar De Oliveira, Diogo Massmann
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation
Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games
Alessandro Zanardi, Pier Giuseppe Sessa, Nando Käslin, Saverio Bolognani, Andrea Censi, Emilio Frazzoli