no code implementations • 28 Oct 2019 • Raha Moraffah, Kai Shu, Adrienne Raglin, Huan Liu
Recent research on deep domain adaptation proposed to mitigate this problem by forcing the deep model to learn more transferable feature representations across domains.
no code implementations • 9 Mar 2020 • Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, Huan Liu
In this work, models that aim to answer causal questions are referred to as causal interpretable models.
no code implementations • 26 Aug 2020 • Raha Moraffah, Bahman Moraffah, Mansooreh Karami, Adrienne Raglin, Huan Liu
The LGN is a GAN-based architecture which learns and samples from the causal model over labels.
no code implementations • 11 Feb 2021 • Raha Moraffah, Paras Sheth, Mansooreh Karami, Anchit Bhattacharya, Qianru Wang, Anique Tahir, Adrienne Raglin, Huan Liu
In this paper, we focus on two causal inference tasks, i. e., treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task.
no code implementations • 1 Jun 2021 • Piyush K. Sharma, Adrienne Raglin
The military is investigating methods to improve communication and agility in its multi-domain operations (MDO).
no code implementations • 2 Jun 2021 • Piyush K. Sharma, Mark Dennison, Adrienne Raglin
Deployment of Internet of Things (IoT) devices and Data Fusion techniques have gained popularity in public and government domains.
no code implementations • 30 Sep 2022 • Paras Sheth, Raha Moraffah, K. Selçuk Candan, Adrienne Raglin, Huan Liu
As a result models that rely on this assumption exhibit poor generalization capabilities.