23 citations · 24 across the 15 of their papers we have counts for
15 papers
Beyond Procrustes distances: a multilinear Gromov-Wasserstein distance capturing chirality
Clément Soubrier, Geoffrey Woollard, Andrew Warren +1
Efficiently and robustly analyzing shape data is critical across many scientific disciplines. While chirality is a fundamental property in numerous applications - most notably in m…
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…
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…
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…
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 Å) is crucial in protein structure determination. Recent advances in deep learning…
ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddings
Chenwei Zhang, Jordan Lovrod, Boyan Beronov +2
Visualization tools can help synthetic biologists and molecular programmers understand the complex reactive pathways of nucleic acid reactions, which can be designed for many poten…
Visualizing DNA reaction trajectories with deep graph embedding approaches
Chenwei Zhang, Khanh Dao Duc, Anne Condon
Synthetic biologists and molecular programmers design novel nucleic acid reactions, with many potential applications. Good visualization tools are needed to help domain experts mak…
EMPOT: partial alignment of density maps and rigid body fitting using unbalanced Gromov-Wasserstein divergence
Aryan Tajmir Riahi, Chenwei Zhang, James Chen +2
Aligning EM density maps and fitting atomic models are essential steps in single particle cryogenic electron microscopy (cryo-EM), with recent methods leveraging various algorithms…
Quantifying Extrinsic Curvature in Neural Manifolds
Francisco Acosta, Sophia Sanborn, Khanh Dao Duc +2
The neural manifold hypothesis postulates that the activity of a neural population forms a low-dimensional manifold whose structure reflects that of the encoded task variables. In…
Testing geometric representation hypotheses from simulated place cell recordings
Thibault Niederhauser, Adam Lester, Nina Miolane +2
Hippocampal place cells can encode spatial locations of an animal in physical or task-relevant spaces. We simulated place cell populations that encoded either Euclidean- or graph-b…
AlignOT: An optimal transport based algorithm for fast 3D alignment with applications to cryogenic electron microscopy density maps
A. Tajmir Riahi, G. Woollard, F. Poitevin +2
Aligning electron density maps from Cryogenic electron microscopy (cryo-EM) is a first key step for studying multiple conformations of a biomolecule. As this step remains costly an…
Defining an action of SO(d)-rotations on images generated by projections of d-dimensional objects: Applications to pose inference with Geometric VAEs
Nicolas Legendre, Khanh Dao Duc, Nina Miolane
Recent advances in variational autoencoders (VAEs) have enabled learning latent manifolds as compact Lie groups, such as . Since this approach assumes that data lies on a su…
ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results
Nina Miolane, Matteo Caorsi, Umberto Lupo +30
This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning…
Operator Norm Inequalities between Tensor Unfoldings on the Partition Lattice
Miaoyan Wang, Khanh Dao Duc, Jonathan Fischer +1
Interest in higher-order tensors has recently surged in data-intensive fields, with a wide range of applications including image processing, blind source separation, community dete…