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
EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
Shengyao Lu, Bang Liu, Keith G. Mills, Jiao He, Di Niu
Generative Active Learning for the Search of Small-molecule Protein Binders
Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, SaiKrishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampášek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin Segler, Michael Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio