5 papers
Recovering Wasserstein Distance Matrices from Few Measurements
Muhammad Rana, Abiy Tasissa, HanQin Cai +2
This paper proposes two algorithms for estimating square Wasserstein distance matrices from a small number of entries. These matrices are used to compute manifold learning embeddin…
Neighbor Embeddings Using Unbalanced Optimal Transport Metrics
Muhammad Rana, Keaton Hamm
This paper proposes the use of the Hellinger--Kantorovich metric from unbalanced optimal transport (UOT) in a dimensionality reduction and learning (supervised and unsupervised) pi…
Multi-Disease Deep Learning Framework for GWAS: Beyond Feature Selection Constraints
Iqra Farooq, Sara Atito, Ayse Demirkan +2
Traditional GWAS has advanced our understanding of complex diseases but often misses nonlinear genetic interactions. Deep learning offers new opportunities to capture complex genom…
Semantic Caching for Improving Web Affordability
Hafsa Akbar, Danish Athar, Muhammad Ayain Fida Rana +4
The rapid growth of web content has led to increasingly large webpages, posing significant challenges for Internet affordability, especially in developing countries where data cost…
Gated-Attention Feature-Fusion Based Framework for Poverty Prediction
Muhammad Umer Ramzan, Wahab Khaddim, Muhammad Ehsan Rana +4
This research paper addresses the significant challenge of accurately estimating poverty levels using deep learning, particularly in developing regions where traditional methods li…