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
Improving the Efficiency of Gradient Descent Algorithms Applied to Optimization Problems with Dynamical Constraints
Ion Matei, Maksym Zhenirovskyy, Johan de Kleer, John Maxwell
Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction
Tong Wu, Jiaqi Wang, Xingang Pan, Xudong Xu, Christian Theobalt, Ziwei Liu, Dahua Lin
Robust and Efficient Depth-based Obstacle Avoidance for Autonomous Miniaturized UAVs
Hanna Müller, Vlad Niculescu, Tommaso Polonelli, Michele Magno, Luca Benini
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment
Chinthaka Dinesh, Gene Cheung, Saghar Bagheri, Ivan V. Bajic
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy
Wenqiang Ruan, Mingxin Xu, Wenjing Fang, Li Wang, Lei Wang, Weili Han
Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury
Margherita Rosnati, Eyal Soreq, Miguel Monteiro, Lucia Li, Neil S. N. Graham, Karl Zimmerman, Carlotta Rossi, Greta Carrara, Guido Bertolini, David J. Sharp, Ben Glocker
Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality
Vudtiwat Ngampruetikorn, David J. Schwab