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, …
Single-cell RNA-sequencing (scRNA-seq) is a powerful tool to quantify transcriptional states in thousands to millions of cells. It is increasingly common for scRNA-seq data to be collected in multiple conditions to measure the effect of an …
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 …
Many fundamental problems in shared-memory distributed computing, including mutual exclusion [8], consensus [18], and implementations of many sequential objects [14], are known to require linear space in the worst case. However, these lower bounds …