Practical Method
Practical methods in machine learning and related fields are currently focused on improving efficiency, accuracy, and generalizability of existing algorithms and models. Research emphasizes developing faster solvers for optimization problems (e.g., using parallel-in-time methods and novel optimizers like the generalized Newton's method), enhancing model robustness through techniques such as low-rank approximations and prompt portfolios, and creating more reliable uncertainty quantification methods. These advancements are crucial for deploying machine learning models in resource-constrained environments and for building more trustworthy and explainable AI systems across diverse applications.
Papers
Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning
Peizheng Li, Hakan Erdol, Keith Briggs, Xiaoyang Wang, Robert Piechocki, Abdelrahim Ahmad, Rui Inacio, Shipra Kapoor, Angela Doufexi, Arjun Parekh
Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method
Mrinal Haloi, Shashank Shekhar, Nikhil Fande, Siddhant Swaroop Dash, Sanjay G
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling
Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, Yi Huang, Junlan Feng, Weiran Xu
ICANet: A Method of Short Video Emotion Recognition Driven by Multimodal Data
Xuecheng Wu, Mengmeng Tian, Lanhang Zhai