Conference

A unsupervised random tree distance for missing data in EHR

The last decade has witnessed a technological arms race to encode the molecular states of cells into DNA libraries, turning DNA sequencers into scalable single-cell microscopes. Single-cell measurement of chromatin accessibility (DNA), gene …

A geometric scattering based network with learnable scale parameters

We propose a method called integrated diffusion for combining multimodal data, gathered via different sensors on the same system, to create a integrated data diffusion operator. As real world data suffers from both local and global noise, we …

A fast diffusion-based earth mover's distance

Biomolecular graph analysis has recently gained much attention in the emerging field of geometric deep learning. While numerous approaches aim to train classifiers that accurately predict molecular properties from graphs that encode their structure, …

We fix unwanted biases in standard deep anomaly detection with a new architecture.

A Combination of Continuous Normalizing Flows and Dynamic Optimal Transport to model the dynamics of cells.

While neural networks are powerful approximators used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features. Here we propose Graph Spectral Regularization for making hidden …

Archetypal analysis is a data decomposition method that describes each observation in a dataset as a convex combination of ''pure types'' or archetypes. These archetypes represent extrema of a data space in which there is a trade-off between …