3 papers
cs.AI2026
Atelier: Learning Local Self-Supervised Features for CryoEM Volumes via Hypernetworks
Phillip Lo, Sudarshan Babu, Dari Kimanius +1
CryoEM map interpretation requires features that are spatially localized, consistent across samples, and informative across spatial scales. Most deep learning methods for map annot…
cs.LG2025
HyperDiffusionFields (HyDiF): Diffusion-Guided Hypernetworks for Learning Implicit Molecular Neural Fields
Sudarshan Babu, Phillip Lo, Xiao Zhang +5
We introduce HyperDiffusionFields (HyDiF), a framework that models 3D molecular conformers as continuous fields rather than discrete atomic coordinates or graphs. At the core of ou…
math.ST2024
Method of Moments for Estimation of Noisy Curves
Phillip Lo, Yuehaw Khoo
In this paper, we study the problem of recovering a ground truth high dimensional piecewise linear curve from a high noise Gaussian point cloud with…