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
Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis
Jia Lin Hau, Erick Delage, Esther Derman, Mohammad Ghavamzadeh, Marek Petrik
An Empirical Analysis of GPT-4V's Performance on Fashion Aesthetic Evaluation
Yuki Hirakawa, Takashi Wada, Kazuya Morishita, Ryotaro Shimizu, Takuya Furusawa, Sai Htaung Kham, Yuki Saito
Evaluating the Performance of a D-Wave Quantum Annealing System for Feature Subset Selection in Software Defect Prediction
Ashis Kumar Mandal, Md Nadim, Chanchal K. Roy, Banani Roy, Kevin A. Schneider
On the Design and Performance of Machine Learning Based Error Correcting Decoders
Yuncheng Yuan, Péter Scheepers, Lydia Tasiou, Yunus Can Gültekin, Federico Corradi, Alex Alvarado