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
Memory Augmented Lookup Dictionary based Language Modeling for Automatic Speech Recognition
Yukun Feng, Ming Tu, Rui Xia, Chuanzeng Huang, Yuxuan Wang
UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions
Asterios Bampakis, Sofia Yfantidou, Athena Vakali
Large-Scale Cell-Level Quality of Service Estimation on 5G Networks Using Machine Learning Techniques
M. Tuğberk İşyapar, Ufuk Uyan, Mahiye Uluyağmur Öztürk
Exploring Vision Transformers as Diffusion Learners
He Cao, Jianan Wang, Tianhe Ren, Xianbiao Qi, Yihao Chen, Yuan Yao, Lei Zhang
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation
Shiyu Wang, Fan Zhou, Yinbo Sun, Lintao Ma, James Zhang, Yangfei Zheng, Bo Zheng, Lei Lei, Yun Hu