Efficient Optimization
Efficient optimization seeks to find the best solution to a problem quickly and with minimal resources, a crucial goal across diverse scientific and engineering domains. Current research focuses on developing and refining algorithms like modified rat swarm optimizers, Bayesian optimization methods, and various neural network architectures tailored to specific problem types (e.g., object detection, reinforcement learning). These advancements improve the speed and accuracy of optimization in applications ranging from medical image analysis and power grid management to drug discovery and large language model training, ultimately impacting the efficiency and effectiveness of numerous fields.
Papers
Optimizing Photoplethysmography-Based Sleep Staging Models by Leveraging Temporal Context for Wearable Devices Applications
Joseph A. P. Quino, Diego A. C. Cardenas, Marcelo A. F. Toledo, Felipe M. Dias, Estela Ribeiro, Jose E. Krieger, Marco A. Gutierrez
Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective
Sarah Packowski, Inge Halilovic, Jenifer Schlotfeldt, Trish Smith
System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization
Jixiang Qing, Becky D Langdon, Robert M Lee, Behrang Shafei, Mark van der Wilk, Calvin Tsay, Ruth Misener
A Study of Optimizations for Fine-tuning Large Language Models
Arjun Singh, Nikhil Pandey, Anup Shirgaonkar, Pavan Manoj, Vijay Aski