Network Programming
Network programming focuses on designing and implementing algorithms and architectures for processing information across interconnected nodes, aiming to efficiently solve complex computational problems. Current research emphasizes developing novel network architectures, such as graph neural networks and deep operator networks, and improving existing algorithms through techniques like frequency domain inference and tensor decomposition for faster and more accurate computations. These advancements are significant for diverse applications, including improved recommendation systems, enhanced anomaly detection in network flows, and more accurate causal inference from network data. The field's impact spans various scientific disciplines and practical applications, driving progress in areas like machine learning, signal processing, and social network analysis.
Papers
Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures
Yuanfang Ren, Yanjun Li, Tyler J. Loftus, Jeremy Balch, Kenneth L. Abbott, Shounak Datta, Matthew M. Ruppert, Ziyuan Guan, Benjamin Shickel, Parisa Rashidi, Tezcan Ozrazgat-Baslanti, Azra Bihorac
Network Fault-tolerant and Byzantine-resilient Social Learning via Collaborative Hierarchical Non-Bayesian Learning
Connor Mclaughlin, Matthew Ding, Denis Edogmus, Lili Su
Edgewise outliers of network indexed signals
Christopher Rieser, Anne Ruiz-Gazen, Christine Thomas-Agnan
PPN: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts
Kaiwen Wei, Jie Yao, Jingyuan Zhang, Yangyang Kang, Fubang Zhao, Yating Zhang, Changlong Sun, Xin Jin, Xin Zhang