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
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness
Javier Poyatos, Daniel Molina, Aitor Martínez, Javier Del Ser, Francisco Herrera
Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems
Baiting Luo, Shreyas Ramakrishna, Ava Pettet, Christopher Kuhn, Gabor Karsai, Ayan Mukhopadhyay
PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics
Max Schrader, Navish Kumar, Nicolas Collignon, Esben Sørig, Soonmyeong Yoon, Akash Srivastava, Kai Xu, Maria Astefanoaei