5 papers
Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis
Gary P. T. Choi, Khanh Dao Duc, Shira Faigenbaum-Golovin +6
A central objective of machine learning is to identify structure and patterns in data. Advances in data acquisition have increasingly produced datasets whose observations possess r…
The Joint Gromov Wasserstein Objective for Multiple Object Matching
Aryan Tajmir Riahi, Khanh Dao Duc
The Gromov-Wasserstein (GW) distance serves as a powerful tool for matching objects in metric spaces. However, its traditional formulation is constrained to pairwise matching betwe…
Scalable Identification and Prioritization of Requisition-Specific Personal Competencies Using Large Language Models
Wanxin Li, Denver McNeney, Nivedita Prabhu +5
AI-powered recruitment tools are increasingly adopted in personnel selection, yet they struggle to capture the requisition (req)-specific personal competencies (PCs) that distingui…
Wasserstein projection distance for fairness testing of regression models
Wanxin Li, Yongjin P. Park, Khanh Dao Duc
Fairness testing evaluates whether a model satisfies a specified fairness criterion across different groups, yet most research has focused on classification models, leaving regress…
CryoSAMU: Enhancing 3D Cryo-EM Density Maps of Protein Structures at Intermediate Resolution with Structure-Aware Multimodal U-Nets
Chenwei Zhang, Khanh Dao Duc
Enhancing cryogenic electron microscopy (cryo-EM) 3D density maps at intermediate resolution (4-8 Ã ) is crucial in protein structure determination. Recent advances in deep learnin…