3 papers
cs.LG2026
PRISM: A 3D Probabilistic Neural Representation for Interpretable Shape Modeling
Yining Jiao, Sreekalyani Bhamidi, Carlton Jude Zdanski +8
Understanding how anatomical shapes evolve in response to developmental covariates - and quantifying their spatially varying uncertainties - is critical in healthcare research. Exi…
cs.LG2025
LucidAtlas: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representations
Yining Jiao, Sreekalyani Bhamidi, Huaizhi Qu +11
The goal of this work is to develop principled techniques to extract information from high dimensional data sets with complex dependencies in areas such as medicine that can provid…
cs.CV2023
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
Yining Jiao, Carlton Zdanski, Julia Kimbell +8
Deep implicit functions (DIFs) have emerged as a powerful paradigm for many computer vision tasks such as 3D shape reconstruction, generation, registration, completion, editing, an…