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
March 31, 2025
Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networks
Kyriakos Stylianopoulos, Paolo Di Lorenzo, George C. AlexandropoulosNational and Kapodistrian University of Athens●Sapienza University●CNITVideo-based Traffic Light Recognition by Rockchip RV1126 for Autonomous Driving
Miao Fan, Xuxu Kong, Shengtong Xu, Haoyi Xiong, Xiangzeng LiuNavInfo Co. Ltd.●Autohome Inc.●Baidu Inc.●Xidian UniversityAI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents
Jiaxiang Chen, Jingwei Shi, Lei Gan, Jiale Zhang, Qingyu Zhang, Dongqian Zhang, Xin Pang, Zhucong Li, Yinghui XuContinue-AI●Fudan University
March 25, 2025
Exploring Disentangled and Controllable Human Image Synthesis: From End-to-End to Stage-by-Stage
Zhengwentai Sun, Heyuan Li, Xihe Yang, Keru Zheng, Shuliang Ning, Yihao Zhi, Hongjie Liao, Chenghong Li, Shuguang Cui, Xiaoguang HanCUHKSZ●CUHKSZMultiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines
Junle Liu, Yun Zhang, Zixi GuoSun Yat-Sen University●Ningbo University
March 24, 2025
March 18, 2025
End-to-End Optimal Detector Design with Mutual Information Surrogates
Kinga Anna Wozniak, Stephen Mulligan, Jan Kieseler, Markus Klute, Francois Fleuret, Tobias GollingUniversity of Geneva●Karlsruhe Institute of Technology●MetaAdaST: Dynamically Adapting Encoder States in the Decoder for End-to-End Speech-to-Text Translation
Wuwei Huang, Dexin Wang, Deyi XiongTianjin UniversityA CNN-based End-to-End Learning for RIS-assisted Communication System
Nipuni Ginige, Nandana Rajatheva, Matti Latva-ahoUniversity of Oulu
March 11, 2025
MsaMIL-Net: An End-to-End Multi-Scale Aware Multiple Instance Learning Network for Efficient Whole Slide Image Classification
Jiangping Wen, Jinyu Wen, Emei FangGuangzhou UniversityReasoning in visual navigation of end-to-end trained agents: a dynamical systems approach
Steeven Janny, Hervé Poirier, Leonid Antsfeld, Guillaume Bono, Gianluca Monaci, Boris Chidlovskii, Francesco Giuliari, Alessio Del Bue, Christian WolfNaver Labs Europe●Istituto Italiano di Tecnologia