New Framework
Recent research focuses on developing versatile frameworks for various tasks, primarily aiming to improve efficiency, reproducibility, and accessibility within their respective domains. These frameworks leverage diverse techniques, including programmatic data generation for LLMs, deep learning architectures for image and audio processing, and reinforcement learning for optimization and automated testing. The resulting advancements enhance the development and evaluation of AI models, improve the reliability of benchmarking processes, and offer new tools for diverse applications ranging from healthcare diagnostics to autonomous vehicle navigation.
Papers
An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction
Urchade Zaratiana, Nadi Tomeh, Pierre Holat, Thierry Charnois
SUDO: a framework for evaluating clinical artificial intelligence systems without ground-truth annotations
Dani Kiyasseh, Aaron Cohen, Chengsheng Jiang, Nicholas Altieri
Spiker+: a framework for the generation of efficient Spiking Neural Networks FPGA accelerators for inference at the edge
Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity
Yiyue Chen, Haris Vikalo, Chianing Wang
Pyreal: A Framework for Interpretable ML Explanations
Alexandra Zytek, Wei-En Wang, Dongyu Liu, Laure Berti-Equille, Kalyan Veeramachaneni
DVIS++: Improved Decoupled Framework for Universal Video Segmentation
Tao Zhang, Xingye Tian, Yikang Zhou, Shunping Ji, Xuebo Wang, Xin Tao, Yuan Zhang, Pengfei Wan, Zhongyuan Wang, Yu Wu
Resource-efficient Generative Mobile Edge Networks in 6G Era: Fundamentals, Framework and Case Study
Bingkun Lai, Jinbo Wen, Jiawen Kang, Hongyang Du, Jiangtian Nie, Changyan Yi, Dong In Kim, Shengli Xie
A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking
Shezheng Song, Shan Zhao, Chengyu Wang, Tianwei Yan, Shasha Li, Xiaoguang Mao, Meng Wang