End to End
"End-to-end" systems aim to streamline complex processes by integrating multiple stages into a single, unified model, eliminating the need for intermediate steps and potentially improving efficiency and performance. Current research focuses on applying this approach across diverse fields, utilizing architectures like transformers, reinforcement learning, and spiking neural networks to tackle challenges in autonomous driving, robotics, speech processing, and natural language processing. This approach offers significant potential for improving the accuracy, speed, and robustness of various applications, while also simplifying development and deployment.
Papers
End-to-End Optimization and Learning of Fair Court Schedules
My H Dinh, James Kotary, Lauryn P. Gouldin, William Yeoh, Ferdinando Fioretto
Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning
Cheima Hammami (INSA Rennes, IETR), Lucas Polo-López (IETR, INSA Rennes), Luc Le Magoarou (INSA Rennes, IETR)
LLM-based Optimization of Compound AI Systems: A Survey
Matthieu Lin, Jenny Sheng, Andrew Zhao, Shenzhi Wang, Yang Yue, Yiran Wu, Huan Liu, Jun Liu, Gao Huang, Yong-Jin Liu
Modelling Concurrent RTP Flows for End-to-end Predictions of QoS in Real Time Communications
Tailai Song, Paolo Garza, Michela Meo, Maurizio Matteo Munafò