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
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning
Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, Mihailo R. Jovanović
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators
Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
Theoretical Analysis on the Efficiency of Interleaved Comparisons
Kojiro Iizuka, Hajime Morita, Makoto P. Kato
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence
Erik C. Johnson, Brian S. Robinson, Gautam K. Vallabha, Justin Joyce, Jordan K. Matelsky, Raphael Norman-Tenazas, Isaac Western, Marisel Villafañe-Delgado, Martha Cervantes, Michael S. Robinette, Arun V. Reddy, Lindsey Kitchell, Patricia K. Rivlin, Elizabeth P. Reilly, Nathan Drenkow, Matthew J. Roos, I-Jeng Wang, Brock A. Wester, William R. Gray-Roncal, Joan A. Hoffmann
NeuManifold: Neural Watertight Manifold Reconstruction with Efficient and High-Quality Rendering Support
Xinyue Wei, Fanbo Xiang, Sai Bi, Anpei Chen, Kalyan Sunkavalli, Zexiang Xu, Hao Su
HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity
Yannan Nellie Wu, Po-An Tsai, Saurav Muralidharan, Angshuman Parashar, Vivienne Sze, Joel S. Emer
ForestTrav: Accurate, Efficient and Deployable Forest Traversability Estimation for Autonomous Ground Vehicles
Fabio Ruetz, Nicholas Lawrance, Emili Hernández, Paulo Borges, Thierry Peynot
PO-VINS: An Efficient and Robust Pose-Only Visual-Inertial State Estimator With LiDAR Enhancement
Hailiang Tang, Tisheng Zhang, Liqiang Wang, Guan Wang, Xiaoji Niu