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
Organizational Bulk Email Systems: Their Role and Performance in Remote Work
Ruoyan Kong, Haiyi Zhu, Joseph A. Konstan
Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores
Pablo Cesar Quihui-Rubio, Daniel Flores-Araiza, Gilberto Ochoa-Ruiz, Miguel Gonzalez-Mendoza, Christian Mata
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations
Rhys Compton, Lily Zhang, Aahlad Puli, Rajesh Ranganath
Comprehensive Assessment of the Performance of Deep Learning Classifiers Reveals a Surprising Lack of Robustness
Michael W. Spratling
Improving Performance of Semi-Supervised Learning by Adversarial Attacks
Dongyoon Yang, Kunwoong Kim, Yongdai Kim
A mixed policy to improve performance of language models on math problems
Gang Chen
A Study on the Performance of Generative Pre-trained Transformer (GPT) in Simulating Depressed Individuals on the Standardized Depressive Symptom Scale
Sijin Cai, Nanfeng Zhang, Jiaying Zhu, Yanjie Liu, Yongjin Zhou
Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models
Lennart Schneider, Bernd Bischl, Janek Thomas