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
Sortformer: Seamless Integration of Speaker Diarization and ASR by Bridging Timestamps and Tokens
Taejin Park, Ivan Medennikov, Kunal Dhawan, Weiqing Wang, He Huang, Nithin Rao Koluguri, Krishna C. Puvvada, Jagadeesh Balam, Boris Ginsburg
One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion
Nico Bohlinger, Grzegorz Czechmanowski, Maciej Krupka, Piotr Kicki, Krzysztof Walas, Jan Peters, Davide Tateo
An End-to-End Approach for Chord-Conditioned Song Generation
Shuochen Gao, Shun Lei, Fan Zhuo, Hangyu Liu, Feng Liu, Boshi Tang, Qiaochu Huang, Shiyin Kang, Zhiyong Wu
End-to-end learned Lossy Dynamic Point Cloud Attribute Compression
Dat Thanh Nguyen, Daniel Zieger, Marc Stamminger, Andre Kaup
An 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
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 Ran
Toward 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