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
Computer Vision for Increased Operative Efficiency via Identification of Instruments in the Neurosurgical Operating Room: A Proof-of-Concept Study
Tanner J. Zachem, Sully F. Chen, Vishal Venkatraman, David AW Sykes, Ravi Prakash, Koumani W. Ntowe, Mikhail A. Bethell, Samantha Spellicy, Alexander D Suarez, Weston Ross, Patrick J. Codd
Efficient Incremental Potential Contact for Actuated Face Simulation
Bo Li, Lingchen Yang, Barbara Solenthaler
Corner-to-Center Long-range Context Model for Efficient Learned Image Compression
Yang Sui, Ding Ding, Xiang Pan, Xiaozhong Xu, Shan Liu, Bo Yuan, Zhenzhong Chen
HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models
Shen Zhang, Zhaowei Chen, Zhenyu Zhao, Yuhao Chen, Yao Tang, Jiajun Liang
BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse Multiview Videos
Georgios Albanis, Nikolaos Zioulis, Kostas Kolomvatsos
Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications
Samira Ghodratnama, Mehrdad Zakershahrak
FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication
Yang Li, Chunhe Xia, Wei Liu, Weidong Zhou, Chen Chen, Tianbo Wang
A Computationally Efficient Sparsified Online Newton Method
Fnu Devvrit, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit Dhillon
Redefining the Laparoscopic Spatial Sense: AI-based Intra- and Postoperative Measurement from Stereoimages
Leopold Müller, Patrick Hemmer, Moritz Queisner, Igor Sauer, Simeon Allmendinger, Johannes Jakubik, Michael Vössing, Niklas Kühl
Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models
Keming Lu, Hongyi Yuan, Runji Lin, Junyang Lin, Zheng Yuan, Chang Zhou, Jingren Zhou
EDMSound: Spectrogram Based Diffusion Models for Efficient and High-Quality Audio Synthesis
Ge Zhu, Yutong Wen, Marc-André Carbonneau, Zhiyao Duan