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
A Computationally Efficient Learning-Based Model Predictive Control for Multirotors under Aerodynamic Disturbances
Babak Akbari, Melissa Greeff
Improving the efficiency of GP-GOMEA for higher-arity operators
Thalea Schlender, Mafalda Malafaia, Tanja Alderliesten, Peter A. N. Bosman
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
Yu Liu, Zibo Wang, Yifei Zhu, Chen Chen
Adaptive Inference: Theoretical Limits and Unexplored Opportunities
Soheil Hor, Ying Qian, Mert Pilanci, Amin Arbabian
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons
Zhenyu Liu, Garrett Gagnon, Swagath Venkataramani, Liu Liu
Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiency
Manuel Muñoz Sánchez, Chris van der Ploeg, Robin Smit, Jos Elfring, Emilia Silvas, René van de Molengraft
Efficient and Interaction-Aware Trajectory Planning for Autonomous Vehicles with Particle Swarm Optimization
Lin Song, David Isele, Naira Hovakimyan, Sangjae Bae
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
Changze Lv, Yansen Wang, Dongqi Han, Xiaoqing Zheng, Xuanjing Huang, Dongsheng Li