Alexander Tong | DeepAI

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∙ 07/07/2023

Simulation-free Schrödinger bridges via score and flow matching

We present simulation-free score and flow matching ([SF]^2M), a simulati...

0 Alexander Tong, et al. ∙

∙ 06/05/2023

Graph Fourier MMD for Signals on Graphs

While numerous methods have been proposed for computing distances betwee...

0 Samuel Leone, et al. ∙

∙ 05/30/2023

A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction

Diffusion-based manifold learning methods have proven useful in represen...

0 Guillaume Huguet, et al. ∙

∙ 02/08/2023

DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks

Learning the causal structure of observable variables is a central focus...

0 Lazar Atanackovic, et al. ∙

∙ 02/01/2023

Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport

Continuous normalizing flows (CNFs) are an attractive generative modelin...

0 Alexander Tong, et al. ∙

∙ 11/02/2022

Geodesic Sinkhorn: optimal transport for high-dimensional datasets

Understanding the dynamics and reactions of cells from population snapsh...

0 Guillaume Huguet, et al. ∙

∙ 08/15/2022

Learnable Filters for Geometric Scattering Modules

We propose a new graph neural network (GNN) module, based on relaxations...

26 Alexander Tong, et al. ∙

∙ 06/29/2022

Manifold Interpolating Optimal-Transport Flows for Trajectory Inference

Here, we present a method called Manifold Interpolating Optimal-Transpor...

0 Guillaume Huguet, et al. ∙

∙ 03/28/2022

Time-inhomogeneous diffusion geometry and topology

Diffusion condensation is a dynamic process that yields a sequence of mu...

19 Guillaume Huguet, et al. ∙

∙ 11/19/2021

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

A major challenge in embedding or visualizing clinical patient data is t...

0 Michal Gerasimiuk, et al. ∙

∙ 07/26/2021

Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance

In modern relational machine learning it is common to encounter large gr...

0 Alexander Tong, et al. ∙

∙ 02/25/2021

Diffusion Earth Mover's Distance and Distribution Embeddings

We propose a new fast method of measuring distances between large number...

0 Alexander Tong, et al. ∙

∙ 10/06/2020

Data-Driven Learning of Geometric Scattering Networks

Graph neural networks (GNNs) in general, and graph convolutional network...

14 Alexander Tong, et al. ∙

∙ 06/12/2020

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

Biomolecular graph analysis has recently gained much attention in the em...

0 Egbert Castro, et al. ∙

∙ 02/09/2020

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics

It is increasingly common to encounter data from dynamic processes captu...

14 Alexander Tong, et al. ∙

∙ 05/26/2019

A Lipschitz-constrained anomaly discriminator framework

Anomaly detection is a problem of great interest in medicine, finance, a...

0 Alexander Tong, et al. ∙

∙ 09/30/2018

Graph Spectral Regularization for Neural Network Interpretability

Deep neural networks can learn meaningful representations of data. Howev...

0 Alexander Tong, et al. ∙

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