Search Results for author:
Found 27 papers, 14 papers with code
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science.
Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy
Entropy and mutual information in neural networks provide rich information on the learning process, but they have proven difficult to compute reliably in high dimensions.
A Computational Framework for Solving Wasserstein Lagrangian Flows
The dynamical formulation of the optimal transport can be extended through various choices of the underlying geometry ($\textit{kinetic energy}$), and the regularization of density paths ($\textit{potential energy}$).
Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems
Understanding causal relationships within Gene Regulatory Networks (GRNs) is essential for unraveling the gene interactions in cellular processes.
SE(3)-Stochastic Flow Matching for Protein Backbone Generation
The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly.
Simulation-free Schrödinger bridges via score and flow matching
We present simulation-free score and flow matching ([SF]$^2$M), a simulation-free objective for inferring stochastic dynamics given unpaired samples drawn from arbitrary source and target distributions.
Graph Fourier MMD for Signals on Graphs
GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes difference in expectation between the pair of distributions on the graph.
Neural FIM for learning Fisher Information Metrics from point cloud data
Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature.
Assessing Neural Network Representations During Training Using Data Diffusion Spectra
We also see that there is an increase in DSMI with the class label over time.
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
Finally, we show that parameters of our more general method can be configured to give results similar to PHATE (a state-of-the-art diffusion based manifold learning method) as well as SNE (an attraction/repulsion neighborhood based method that forms the basis of t-SNE).
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
In this paper we leverage the fact that it is possible to estimate the "velocity" of gene expression with RNA velocity techniques to develop an approach that addresses both challenges.
Improving and generalizing flow-based generative models with minibatch optimal transport
CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models.
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds
Efficient computation of optimal transport distance between distributions is of growing importance in data science.
Learnable Filters for Geometric Scattering Modules
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters.
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
In GAE the latent space distance between points is regularized to match a novel multiscale geodesic distance on the data manifold that we define.
Time-inhomogeneous diffusion geometry and topology
From a geometric perspective, we obtain convergence bounds based on the smallest transition probability and the radius of the data, whereas from a spectral perspective, our bounds are based on the eigenspectrum of the diffusion kernel.
MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data
In particular, in EHR data, some variables are {\em missing not at random (MNAR)} but deliberately not collected and thus are a source of information.
Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance
We propose to compare and organize such datasets of graph signals by using an earth mover's distance (EMD) with a geodesic cost over the underlying graph.
Diffusion Earth Mover's Distance and Distribution Embeddings
Here, Diffusion EMD can derive distances between patients on the manifold of cells at least two orders of magnitude faster than equally accurate methods.
Multimodal Data Visualization and Denoising with Integrated Diffusion
We propose a method called integrated diffusion for combining multimodal datasets, or data gathered via several different measurements on the same system, to create a joint data diffusion operator.
Data-Driven Learning of Geometric Scattering Networks
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters.
Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings
We propose a geometric scattering autoencoder (GSAE) network for learning such graph embeddings.
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
To address this issue, we establish a link between continuous normalizing flows and dynamic optimal transport, that allows us to model the expected paths of points over time.
Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms
As a result, the proposed construction unifies and extends known theoretical results for many of the existing graph scattering architectures.
Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators
We show that this procedure successfully detects unseen anomalies with guarantees on those that have a certain Wasserstein distance from the data or corrupted training set.
Graph Spectral Regularization For Neural Network Interpretability
First, we show a synthetic example that the graph-structured layer can reveal topological features of the data.
Interpretable Neuron Structuring with Graph Spectral Regularization
Taking inspiration from spatial organization and localization of neuron activations in biological networks, we use a graph Laplacian penalty to structure the activations within a layer.
