Large Scale
Large-scale data processing and analysis are central to addressing numerous scientific and engineering challenges, focusing on efficient handling of massive datasets and complex systems. Current research emphasizes developing novel algorithms and model architectures, such as graph neural networks, deep learning models, and physics-guided machine learning, to improve efficiency, accuracy, and scalability in diverse applications. These advancements are crucial for tackling problems ranging from traffic optimization and robot navigation to astronomical surveys and the development of more energy-efficient AI systems. The resulting insights and tools have significant implications across various fields, enabling more effective data-driven decision-making and scientific discovery.
Papers
Inter-Cell Network Slicing With Transfer Learning Empowered Multi-Agent Deep Reinforcement Learning
Tianlun Hu, Qi Liao, Qiang Liu, Georg Carle
CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality
Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Rongyu Cao, Binhua Li, Fei Huang, Yongbin Li
Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry
Bariscan Bozkurt, Cengiz Pehlevan, Alper T Erdogan
ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systems
Yi Zhang, Jan Deriu, George Katsogiannis-Meimarakis, Catherine Kosten, Georgia Koutrika, Kurt Stockinger
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
Giacomo Meanti, Antoine Chatalic, Vladimir R. Kostic, Pietro Novelli, Massimiliano Pontil, Lorenzo Rosasco