Conference

MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data

A unsupervised random tree distance for missing data in EHR

A sandbox for prediction and integration of DNA, RNA, and protein data in single cells

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 …

Data-Driven Learning of Geometric Scattering Networks

A geometric scattering based network with learnable scale parameters

Multimodal data visualization and denoising with integrated diffusion

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 …

Diffusion Earth Mover's Distance and Distribution Embeddings

A fast diffusion-based earth mover's distance

Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings

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, …

Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators

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

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics

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

Interpretable Neuron Structuring with Graph Spectral Regularization

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 …

Finding Archetypal Spaces Using Neural Networks

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 …