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
FollowMe: a Robust Person Following Framework Based on Re-Identification and Gestures
Federico Rollo, Andrea Zunino, Gennaro Raiola, Fabio Amadio, Arash Ajoudani, Nikolaos Tsagarakis
IMGTB: A Framework for Machine-Generated Text Detection Benchmarking
Michal Spiegel, Dominik Macko
Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework
Florian Felten, El-Ghazali Talbi, Grégoire Danoy
A Framework for Modeling, Analyzing, and Decision-Making in Disease Spread Dynamics and Medicine/Vaccine Distribution
Zenin Easa Panthakkalakath, Neeraj, Jimson Mathew
A Framework for Monitoring and Retraining Language Models in Real-World Applications
Jaykumar Kasundra, Claudia Schulz, Melicaalsadat Mirsafian, Stavroula Skylaki
Zenkai -- Framework For Exploring Beyond Backpropagation
Greg Short
Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness
Ahmed Emam, Mohamed Farag, Ribana Roscher
OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued Pretraining
Yihong Liu, Peiqin Lin, Mingyang Wang, Hinrich Schütze