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
Investigating the Effect of Network Pruning on Performance and Interpretability
Jonathan von Rad, Florian Seuffert
Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training
Abhijeet Patil, Harsh Diwakar, Jay Sawant, Nikhil Cherian Kurian, Subhash Yadav, Swapnil Rane, Tripti Bameta, Amit Sethi
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
Jacobus G. M. van der Linden, Daniël Vos, Mathijs M. de Weerdt, Sicco Verwer, Emir Demirović
Performance and Power: Systematic Evaluation of AI Workloads on Accelerators with CARAML
Chelsea Maria John, Stepan Nassyr, Carolin Penke, Andreas Herten
On the Computation of BD-Rate over a Set of Videos for Fair Assessment of Performance of Learned Video Codecs
M.Akin Yilmaz, Onur Keleş, A.Murat Tekalp
Co-Optimization of Robot Design and Control: Enhancing Performance and Understanding Design Complexity
Etor Arza, Frank Veenstra, Tønnes F. Nygaard, Kyrre Glette
CubicML: Automated ML for Large ML Systems Co-design with ML Prediction of Performance
Wei Wen, Quanyu Zhu, Weiwei Chu, Wen-Yen Chen, Jiyan Yang
The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
Alberto Cattaneo, Stephen Bonner, Thomas Martynec, Carlo Luschi, Ian P Barrett, Daniel Justus