Search Results for author:
Found 9 papers, 1 papers with code
Graph topological property recovery with heat and wave dynamics-based features on graphs
In this paper, we propose Graph Differential Equation Network (GDeNet), an approach that harnesses the expressive power of solutions to PDEs on a graph to obtain continuous node- and graph-level representations for various downstream tasks.
A Flow Artist for High-Dimensional Cellular Data
Existing embedding techniques either do not utilize velocity information or embed the coordinates and velocities independently, i. e., they either impose velocities on top of an existing point embedding or embed points within a prescribed vector field.
Inferring dynamic regulatory interaction graphs from time series data with perturbations
Complex systems are characterized by intricate interactions between entities that evolve dynamically over time.
Topological Data Analysis of Spatial Patterning in Heterogeneous Cell Populations: Clustering and Sorting with Varying Cell-Cell Adhesion
We further demonstrate that persistence images can be normalized to improve classification for simulations with varying cell numbers due to proliferation.
Learnable Filters for Geometric Scattering Modules
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters.
Diffusion Curvature for Estimating Local Curvature in High Dimensional Data
We introduce a new intrinsic measure of local curvature on point-cloud data called diffusion curvature.
ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins
Using ReLSO, we explicitly model the sequence-function landscape of large labeled datasets and generate new molecules by optimizing within the latent space using gradient-based methods.
Molecular Graph Generation via Geometric Scattering
We guide the latent representation of an autoencoder by capturing graph structure information with the geometric scattering transform and apply penalties that structure the representation also by molecular properties.
Topological Data Analysis of Collective and Individual Epithelial Cells using Persistent Homology of Loops
Instead, topological data analysis (TDA) determines the stability of spatial connectivity at varying length scales (i. e. persistent homology) and can compare different particle configurations based on the "cost" of reorganizing one configuration into another.
