Tag
This paper proposes an integrated deep learning and statistical framework for associating gene-environment interactions with whole-network leaf vascular architecture, using EDTER for edge detection and SSCCA for variable selection.
This paper introduces Multilingual-IRT, a statistical framework extending Item Response Theory with per-language difficulty deviations and split discriminability, enabling efficient prediction of unobserved evaluations, detection of translation errors, and recovery of culture-specific items across 29 languages.
This paper investigates the Platonic Representation Hypothesis, proposing that alignment arises from linear structure in representations, and introduces a statistical framework of signal, bias, and noise.