General Strategy
Research on general strategies focuses on developing and optimizing methods for achieving specific goals across diverse domains, from mitigating online trolling to enhancing the efficiency of AI systems. Current efforts concentrate on leveraging human preferences to guide strategy selection, integrating sustainability considerations into AI development, and employing techniques like imitation learning and Bayesian optimization to improve model performance and efficiency. These advancements have significant implications for various fields, improving online community management, promoting responsible AI development, and accelerating scientific discovery through more efficient computational tools.
Papers
Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning
Cristian Ramirez-Atencia, Javier Del Ser, David Camacho
Deep Confident Steps to New Pockets: Strategies for Docking Generalization
Gabriele Corso, Arthur Deng, Benjamin Fry, Nicholas Polizzi, Regina Barzilay, Tommi Jaakkola
Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource Limitations
Gregor Donabauer, Udo Kruschwitz
Locality enhanced dynamic biasing and sampling strategies for contextual ASR
Md Asif Jalal, Pablo Peso Parada, George Pavlidis, Vasileios Moschopoulos, Karthikeyan Saravanan, Chrysovalantis-Giorgos Kontoulis, Jisi Zhang, Anastasios Drosou, Gil Ho Lee, Jungin Lee, Seokyeong Jung
Methods and strategies for improving the novel view synthesis quality of neural radiation field
Shun Fang, Ming Cui, Xing Feng, Yanna Lv