System Performance
System performance research focuses on optimizing the efficiency and accuracy of various computational systems, from machine learning models to robotic controllers and even quantum computers. Current research emphasizes improving model architectures (e.g., graph-oriented databases for language models, retention-based networks for multi-agent reinforcement learning) and training techniques (e.g., hard sample mining, co-optimization of design and control), while also addressing issues like fairness, robustness, and explainability. These advancements have significant implications for diverse fields, impacting the development of more efficient and reliable AI systems, improved medical diagnostics, and enhanced manufacturing processes.
Papers
Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications
Arian Bakhtiarnia, Błażej Leporowski, Lukas Esterle, Alexandros Iosifidis
Measuring and signing fairness as performance under multiple stakeholder distributions
David Lopez-Paz, Diane Bouchacourt, Levent Sagun, Nicolas Usunier
Study of the performance and scalability of federated learning for medical imaging with intermittent clients
Judith Sáinz-Pardo Díaz, Álvaro López García
MLGOPerf: An ML Guided Inliner to Optimize Performance
Amir H. Ashouri, Mostafa Elhoushi, Yuzhe Hua, Xiang Wang, Muhammad Asif Manzoor, Bryan Chan, Yaoqing Gao
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents
Nathaniel Hamilton, Kyle Dunlap, Taylor T Johnson, Kerianne L Hobbs
On Improving the Performance of Glitch Classification for Gravitational Wave Detection by using Generative Adversarial Networks
Jianqi Yan, Alex P. Leung, David C. Y. Hui
DeepPERF: A Deep Learning-Based Approach For Improving Software Performance
Spandan Garg, Roshanak Zilouchian Moghaddam, Colin B. Clement, Neel Sundaresan, Chen Wu
Zero Stability Well Predicts Performance of Convolutional Neural Networks
Liangming Chen, Long Jin, Mingsheng Shang
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift
Christina Baek, Yiding Jiang, Aditi Raghunathan, Zico Kolter