44 citations · 253 across the 39 of their papers we have counts for
6 papers · 1 filter
Data-Driven Learning of Geometric Scattering Networks
Alexander Tong, Frederik Wenkel, Kincaid MacDonald +2
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.…
Image-to-image Mapping with Many Domains by Sparse Attribute Transfer
Matthew Amodio, Rim Assouel, Victor Schmidt +3
Unsupervised image-to-image translation consists of learning a pair of mappings between two domains without known pairwise correspondences between points. The current convention is…
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
Bastian Rieck, Tristan Yates, Christian Bock +4
Functional magnetic resonance imaging (fMRI) is a crucial technology for gaining insights into cognitive processes in humans. Data amassed from fMRI measurements result in volumetr…
Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings
Egbert Castro, Andrew Benz, Alexander Tong +2
Biomolecular graph analysis has recently gained much attention in the emerging field of geometric deep learning. Here we focus on organizing biomolecular graphs in ways that expose…
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
Alexander Tong, Jessie Huang, Guy Wolf +2
It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts t…
Making Logic Learnable With Neural Networks
Tobias Brudermueller, Dennis L. Shung, Adrian J. Stanley +2
While neural networks are good at learning unspecified functions from training samples, they cannot be directly implemented in hardware and are often not interpretable or formally…