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
Meta-Gradient Search Control: A Method for Improving the Efficiency of Dyna-style Planning
Bradley Burega, John D. Martin, Luke Kapeluck, Michael Bowling
Efficient and Distributed Large-Scale 3D Map Registration using Tomographic Features
Halil Utku Unlu, Anthony Tzes, Prashanth Krishnamurthy, Farshad Khorrami
Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training
Xinyu Lian, Sam Ade Jacobs, Lev Kurilenko, Masahiro Tanaka, Stas Bekman, Olatunji Ruwase, Minjia Zhang
Transforming Software Development: Evaluating the Efficiency and Challenges of GitHub Copilot in Real-World Projects
Ruchika Pandey, Prabhat Singh, Raymond Wei, Shaila Shankar
Efficient and Effective Implicit Dynamic Graph Neural Network
Yongjian Zhong, Hieu Vu, Tianbao Yang, Bijaya Adhikari
Towards Efficient and Scalable Training of Differentially Private Deep Learning
Sebastian Rodriguez Beltran, Marlon Tobaben, Joonas Jälkö, Niki Loppi, Antti Honkela
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
Philipp Becker, Niklas Freymuth, Gerhard Neumann
A Provably Efficient Option-Based Algorithm for both High-Level and Low-Level Learning
Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli
FC3DNet: A Fully Connected Encoder-Decoder for Efficient Demoir'eing
Zhibo Du, Long Peng, Yang Wang, Yang Cao, Zheng-Jun Zha
xCOMET-lite: Bridging the Gap Between Efficiency and Quality in Learned MT Evaluation Metrics
Daniil Larionov, Mikhail Seleznyov, Vasiliy Viskov, Alexander Panchenko, Steffen Eger
RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold
Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar