We present simulation-free score and flow matching ([SF]^2M), a
simulati...
While numerous methods have been proposed for computing distances betwee...
Diffusion-based manifold learning methods have proven useful in
represen...
Learning the causal structure of observable variables is a central focus...
Continuous normalizing flows (CNFs) are an attractive generative modelin...
Understanding the dynamics and reactions of cells from population snapsh...
We propose a new graph neural network (GNN) module, based on relaxations...
Here, we present a method called Manifold Interpolating Optimal-Transpor...
Diffusion condensation is a dynamic process that yields a sequence of
mu...
A major challenge in embedding or visualizing clinical patient data is t...
In modern relational machine learning it is common to encounter large gr...
We propose a new fast method of measuring distances between large number...
Graph neural networks (GNNs) in general, and graph convolutional network...
Biomolecular graph analysis has recently gained much attention in the
em...
It is increasingly common to encounter data from dynamic processes captu...
Anomaly detection is a problem of great interest in medicine, finance, a...
Deep neural networks can learn meaningful representations of data. Howev...