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
New Metrics for Assessing Projection Pursuit Indexes, and Guiding Optimisation Choices
H. Sherry Zhang, Dianne Cook, Nicolas Langrené, Jessica Wai Yin Leung
Can Open-Source LLMs Compete with Commercial Models? Exploring the Few-Shot Performance of Current GPT Models in Biomedical Tasks
Samy Ateia, Udo Kruschwitz
BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillation
Zekai Xu, Kang You, Qinghai Guo, Xiang Wang, Zhezhi He
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization
Yuyang Ye, Lu-An Tang, Haoyu Wang, Runlong Yu, Wenchao Yu, Erhu He, Haifeng Chen, Hui Xiong
On Evaluating The Performance of Watermarked Machine-Generated Texts Under Adversarial Attacks
Zesen Liu, Tianshuo Cong, Xinlei He, Qi Li
Are Large Language Models Strategic Decision Makers? A Study of Performance and Bias in Two-Player Non-Zero-Sum Games
Nathan Herr, Fernando Acero, Roberta Raileanu, María Pérez-Ortiz, Zhibin Li
UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis
Ruslan Zhagypar, Nour Kouzayha, Hesham ElSawy, Hayssam Dahrouj, Tareq Y. Al-Naffouri
M5 -- A Diverse Benchmark to Assess the Performance of Large Multimodal Models Across Multilingual and Multicultural Vision-Language Tasks
Florian Schneider, Sunayana Sitaram
On the performance of sequential Bayesian update for database of diverse tsunami scenarios
Reika Nomura, Louise A. Hirao Vermare, Saneiki Fujita, Donsub Rim, Shuji Moriguchi, Randall J. LeVeque, Kenjiro Terada