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
Mastering Strategy Card Game (Legends of Code and Magic) via End-to-End Policy and Optimistic Smooth Fictitious Play
Wei Xi, Yongxin Zhang, Changnan Xiao, Xuefeng Huang, Shihong Deng, Haowei Liang, Jie Chen, Peng Sun
An End-to-End Approach for Online Decision Mining and Decision Drift Analysis in Process-Aware Information Systems: Extended Version
Beate Scheibel, Stefanie Rinderle-Ma
Scalable End-to-End ML Platforms: from AutoML to Self-serve
Igor L. Markov, Pavlos A. Apostolopoulos, Mia R. Garrard, Tanya Qie, Yin Huang, Tanvi Gupta, Anika Li, Cesar Cardoso, George Han, Ryan Maghsoudian, Norm Zhou
Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generator
Vladimir Bataev, Roman Korostik, Evgeny Shabalin, Vitaly Lavrukhin, Boris Ginsburg
Self Correspondence Distillation for End-to-End Weakly-Supervised Semantic Segmentation
Rongtao Xu, Changwei Wang, Jiaxi Sun, Shibiao Xu, Weiliang Meng, Xiaopeng Zhang
Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech Recognition
Yuchen Hu, Chen Chen, Ruizhe Li, Qiushi Zhu, Eng Siong Chng
Unifying Speech Enhancement and Separation with Gradient Modulation for End-to-End Noise-Robust Speech Separation
Yuchen Hu, Chen Chen, Heqing Zou, Xionghu Zhong, Eng Siong Chng