Dec 17, 2023 · We propose a unified framework that covers the uncertainties emerging in both the input feature space of the ML models and the COs.

The framework is described as a robust optimization problem and is prac- tically solved via end-to-end adversarial training (E2E-AT). Finally, the performance ...

E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning Wangkun Xu1, Jianhong Wang2, Fei Teng1 1Department of EEE, Imperial ...

This is the official repo for the paper E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning, to be appeared in AAAI-24.

Apr 4, 2024 · The framework is described as a robust optimization problem and is practically solved via end-to-end adversarial training (E2E-AT). Finally, the ...

Feb 22, 2024 · E2E-AT: A Unified Framework for Tackling Uncertainty in Task-Aware End-to-End Learning | VIDEO ... A Generalized Neural Diffusion Framework ...

Dec 23, 2023 · This paper proposes a unified framework for tackling uncer- tainties in task-aware E2E learning. We argue that the un- certainties occur at both ...

E2E-AT: A Unified Framework for Tackling Uncertainty in Task-Aware End-to-End Learning. W Xu, J Wang, F Teng. Proceedings of the AAAI Conference on Artificial ...

E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning ... Successful machine learning involves a complete pipeline of data ...

E2E-AT: A Unified Framework for Tackling Uncertainty in Task-Aware End-to-End Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 38(14) ...