229 citations · 624 across the 50 of their papers we have counts for
11 papers · 1 filter
Manifold Alignment with Label Information
Andres F. Duque, Myriam Lizotte, Guy Wolf +1
Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integration of distinct data-views can be used f…
Diffusion Transport Alignment
Andres F. Duque, Guy Wolf, Kevin R. Moon
The integration of multimodal data presents a challenge in cases when the study of a given phenomena by different instruments or conditions generates distinct but related domains.…
Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks
Frederik Wenkel, Yimeng Min, Matthew Hirn +2
Geometric deep learning has made great strides towards generalizing the design of structure-aware neural networks from traditional domains to non-Euclidean ones, giving rise to gra…
Extendable and invertible manifold learning with geometry regularized autoencoders
Andrés F. Duque, Sacha Morin, Guy Wolf +1
A fundamental task in data exploration is to extract simplified low dimensional representations that capture intrinsic geometry in data, especially for faithfully visualizing data…
Supervised Visualization for Data Exploration
Jake S. Rhodes, Adele Cutler, Guy Wolf +1
Dimensionality reduction is often used as an initial step in data exploration, either as preprocessing for classification or regression or for visualization. Most dimensionality re…
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…