Search Query
Search query optimization is a broad field aiming to improve the efficiency and effectiveness of information retrieval across diverse applications, from game playing and code generation to scientific literature exploration and medical image analysis. Current research focuses on developing novel algorithms, such as adaptive Monte Carlo Tree Search and various transformer-based architectures, to enhance search strategies and reduce computational costs. These advancements have significant implications for various fields, improving the speed and accuracy of tasks ranging from AI decision-making to large-scale data analysis and medical diagnosis.
Papers
A Search for Nonlinear Balanced Boolean Functions by Leveraging Phenotypic Properties
Bruno Gašperov, Marko Đurasević, Domagoj Jakobović
Behavioral Cloning via Search in Embedded Demonstration Dataset
Federico Malato, Florian Leopold, Ville Hautamaki, Andrew Melnik
In Search of netUnicorn: A Data-Collection Platform to Develop Generalizable ML Models for Network Security Problems
Roman Beltiukov, Wenbo Guo, Arpit Gupta, Walter Willinger
RescueSpeech: A German Corpus for Speech Recognition in Search and Rescue Domain
Sangeet Sagar, Mirco Ravanelli, Bernd Kiefer, Ivana Kruijff Korbayova, Josef van Genabith
ColdNAS: Search to Modulate for User Cold-Start Recommendation
Shiguang Wu, Yaqing Wang, Qinghe Jing, Daxiang Dong, Dejing Dou, Quanming Yao