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.
570papers
Papers - Page 8
August 20, 2024
End-to-end learned Lossy Dynamic Point Cloud Attribute Compression
Dat Thanh Nguyen, Daniel Zieger, Marc Stamminger, Andre KaupAn End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing
Xinlang Yue, Yiran Liu, Fangzhou Shi, Sihong Luo, Chen Zhong, Min Lu, Zhe Xu
August 17, 2024
V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models
Junwei You, Haotian Shi, Zhuoyu Jiang, Zilin Huang, Rui Gan, Keshu Wu, Xi Cheng, Xiaopeng Li, Bin RanToward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks
Yongqi Ding, Lin Zuo, Mengmeng Jing, Kunshan Yang, Biao Chen, Yunqian Yu
August 7, 2024
August 6, 2024
July 31, 2024