1 code implementation • 12 Jun 2023 • Vinod Kumar Chauhan, Jiandong Zhou, Ping Lu, Soheila Molaei, David A. Clifton
They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks.
1 code implementation • 25 May 2023 • Vinod Kumar Chauhan, Jiandong Zhou, Ghadeer Ghosheh, Soheila Molaei, David A. Clifton
To tackle this problem, we propose a deep learning framework based on `\textit{soft weight sharing}' to train ITE learners, enabling \textit{dynamic end-to-end} information sharing among treatment groups.
no code implementations • 19 Oct 2022 • Vinod Kumar Chauhan, Soheila Molaei, Marzia Hoque Tania, Anshul Thakur, Tingting Zhu, David A. Clifton
Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc.
no code implementations • 23 Feb 2019 • Soheila Molaei, Hadi Zare, Hadi Veisi
In this paper, information diffusion is considered through a latent representation learning of the heterogeneous networks to encode in a deep learning model.