research
∙
09/18/2023
Graph topological property recovery with heat and wave dynamics-based features on graphs
In this paper, we propose Graph Differential Equation Network (GDeNet), ...
share
research
∙
07/07/2023
Simulation-free Schrödinger bridges via score and flow matching
We present simulation-free score and flow matching ([SF]^2M), a simulati...
research
∙
06/13/2023
Inferring dynamic regulatory interaction graphs from time series data with perturbations
Complex systems are characterized by intricate interactions between enti...
research
∙
06/05/2023
Graph Fourier MMD for Signals on Graphs
While numerous methods have been proposed for computing distances betwee...
research
∙
05/30/2023
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
Diffusion-based manifold learning methods have proven useful in represen...
research
∙
02/01/2023
Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport
Continuous normalizing flows (CNFs) are an attractive generative modelin...
research
∙
11/02/2022
Geodesic Sinkhorn: optimal transport for high-dimensional datasets
Understanding the dynamics and reactions of cells from population snapsh...
research
∙
10/28/2022
Reliability of CKA as a Similarity Measure in Deep Learning
Comparing learned neural representations in neural networks is a challen...
research
∙
10/23/2022
Manifold Alignment with Label Information
Multi-domain data is becoming increasingly common and presents both chal...
research
∙
08/15/2022
Learnable Filters for Geometric Scattering Modules
We propose a new graph neural network (GNN) module, based on relaxations...
research
∙
06/29/2022
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
Here, we present a method called Manifold Interpolating Optimal-Transpor...
research
∙
06/15/2022
Taxonomy of Benchmarks in Graph Representation Learning
Graph Neural Networks (GNNs) extend the success of neural networks to gr...
research
∙
06/15/2022
Diffusion Transport Alignment
The integration of multimodal data presents a challenge in cases when th...
research
∙
06/03/2022
Can Hybrid Geometric Scattering Networks Help Solve the Maximal Clique Problem?
We propose a geometric scattering-based graph neural network (GNN) for a...
research
∙
03/28/2022
Time-inhomogeneous diffusion geometry and topology
Diffusion condensation is a dynamic process that yields a sequence of mu...
research
∙
01/22/2022
Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks
Geometric deep learning (GDL) has made great strides towards generalizin...
research
∙
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...
research
∙
11/07/2021
Positivity Validation Detection and Explainability via Zero Fraction Multi-Hypothesis Testing and Asymmetrically Pruned Decision Trees
Positivity is one of the three conditions for causal inference from obse...
research
∙
10/27/2021
Towards a Taxonomy of Graph Learning Datasets
Graph neural networks (GNNs) have attracted much attention due to their ...
research
∙
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...
research
∙
07/20/2021
Parametric Scattering Networks
The wavelet scattering transform creates geometric invariants and deform...
research
∙
07/15/2021
Hierarchical graph neural nets can capture long-range interactions
Graph neural networks (GNNs) based on message passing between neighborin...
research
∙
02/25/2021
Diffusion Earth Mover's Distance and Distribution Embeddings
We propose a new fast method of measuring distances between large number...
research
∙
02/12/2021
Multimodal data visualization, denoising and clustering with integrated diffusion
We propose a method called integrated diffusion for combining multimodal...
research
∙
01/31/2021
Visualizing High-Dimensional Trajectories on the Loss-Landscape of ANNs
Training artificial neural networks requires the optimization of highly ...
research
∙
10/28/2020
Geometric Scattering Attention Networks
Geometric scattering has recently gained recognition in graph representa...
research
∙
10/06/2020
Data-Driven Learning of Geometric Scattering Networks
Graph neural networks (GNNs) in general, and graph convolutional network...
research
∙
07/14/2020
Extendable and invertible manifold learning with geometry regularized autoencoders
A fundamental task in data exploration is to extract simplified low dime...
research
∙
06/22/2020
Advantages of biologically-inspired adaptive neural activation in RNNs during learning
Dynamic adaptation in single-neuron response plays a fundamental role in...
research
∙
06/15/2020
Supervised Visualization for Data Exploration
Dimensionality reduction is often used as an initial step in data explor...
research
∙
06/14/2020
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
Functional magnetic resonance imaging (fMRI) is a crucial technology for...
research
∙
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...
research
∙
03/18/2020
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks
Graph convolutional networks (GCNs) have shown promising results in proc...
research
∙
02/17/2020
Identifying Critical Neurons in ANN Architectures using Mixed Integer Programming
We introduce a novel approach to optimize the architecture of deep neura...
research
∙
02/09/2020
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
It is increasingly common to encounter data from dynamic processes captu...
research
∙
01/09/2020
Internal representation dynamics and geometry in recurrent neural networks
The efficiency of recurrent neural networks (RNNs) in dealing with seque...
research
∙
11/14/2019
Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms
The scattering transform is a multilayered wavelet-based deep learning a...
research
∙
07/10/2019
Coarse Graining of Data via Inhomogeneous Diffusion Condensation
Big data often has emergent structure that exists at multiple levels of ...
research
∙
06/25/2019
Visualizing High Dimensional Dynamical Processes
Manifold learning techniques for dynamical systems and time series have ...
research
∙
05/26/2019
A Lipschitz-constrained anomaly discriminator framework
Anomaly detection is a problem of great interest in medicine, finance, a...
research
∙
05/24/2019
Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds
The Euclidean scattering transform was introduced nearly a decade ago to...
research
∙
01/31/2019
Compressed Diffusion
Diffusion maps are a commonly used kernel-based method for manifold lear...
research
∙
01/25/2019
Finding Archetypal Spaces for Data Using Neural Networks
Archetypal analysis is a type of factor analysis where data is fit by a ...
research
∙
12/15/2018
Geometric Scattering on Manifolds
We present a mathematical model for geometric deep learning based upon a...
research
∙
10/07/2018
Graph Classification with Geometric Scattering
One of the most notable contributions of deep learning is the applicatio...
research
∙
09/30/2018
Graph Spectral Regularization for Neural Network Interpretability
Deep neural networks can learn meaningful representations of data. Howev...
research
∙
09/30/2018
Manifold Alignment with Feature Correspondence
We propose a novel framework for combining datasets via alignment of the...
research
∙
02/14/2018
Geometry-Based Data Generation
Many generative models attempt to replicate the density of their input d...
research
∙
02/10/2018
Modeling Dynamics with Deep Transition-Learning Networks
Markov processes, both classical and higher order, are often used to mod...
research
∙
11/19/2015