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
Explainable Goal Recognition: A Framework Based on Weight of Evidence
Abeer Alshehri, Tim Miller, Mor Vered
FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning
Yi-Rui Yang, Kun Wang, Wu-Jun Li
A Framework for History-Aware Hyperparameter Optimisation in Reinforcement Learning
Juan Marcelo Parra-Ullauri, Chen Zhen, Antonio García-Domínguez, Nelly Bencomo, Changgang Zheng, Juan Boubeta-Puig, Guadalupe Ortiz, Shufan Yang
A Framework for Neurosymbolic Robot Action Planning using Large Language Models
Alessio Capitanelli, Fulvio Mastrogiovanni
TAU: A Framework for Video-Based Traffic Analytics Leveraging Artificial Intelligence and Unmanned Aerial Systems
Bilel Benjdira, Anis Koubaa, Ahmad Taher Azar, Zahid Khan, Adel Ammar, Wadii Boulila
A framework for benchmarking class-out-of-distribution detection and its application to ImageNet
Ido Galil, Mohammed Dabbah, Ran El-Yaniv
A Framework for Unified Real-time Personalized and Non-Personalized Speech Enhancement
Zhepei Wang, Ritwik Giri, Devansh Shah, Jean-Marc Valin, Michael M. Goodwin, Paris Smaragdis