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Found 4 papers, 2 papers with code
A Brief Review of Hypernetworks in Deep Learning
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.
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation
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.
Adversarial De-confounding in Individualised Treatment Effects Estimation
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.
Deep Learning Approach on Information Diffusion in Heterogeneous Networks
In this paper, information diffusion is considered through a latent representation learning of the heterogeneous networks to encode in a deep learning model.
