235 citations · 531 across the 38 of their papers we have counts for
6 papers · 1 filter
A generic physics-informed neural network-based framework for reliability assessment of multi-state systems
Taotao Zhou, Xiaoge Zhang, Enrique Lopez Droguett +1
In this paper, we leverage the recent advances in physics-informed neural network (PINN) and develop a generic PINN-based framework to assess the reliability of multi-state systems…
Class-wise Thresholding for Robust Out-of-Distribution Detection
Matteo Guarrera, Baihong Jin, Tung-Wei Lin +3
We consider the problem of detecting OoD(Out-of-Distribution) input data when using deep neural networks, and we propose a simple yet effective way to improve the robustness of sev…
Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data
Hari Prasanna Das, Ryan Tran, Japjot Singh +4
At the onset of a pandemic, such as COVID-19, data with proper labeling/attributes corresponding to the new disease might be unavailable or sparse. Machine L…
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments
Abdus Salam Azad, Edward Kim, Qiancheng Wu +4
The capability of a reinforcement learning (RL) agent heavily depends on the diversity of the learning scenarios generated by the environment. Generation of diverse realistic scena…
Hypercontracts
Inigo Incer, Albert Benveniste, Alberto Sangiovanni-Vincentelli +1
Contract theories have been proposed to formally support distributed and decentralized system design while ensuring safe system integration. In this paper we propose hypercontracts…
A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions
Corvin Deboeser, Jordan Ivanchev, Thomas Braud +3
This paper introduces the SEAD framework that simplifies the process of designing and describing autonomous vehicle platooning manoeuvres. Although a large body of research has bee…