b'Guy Wolf'

research

∙ 06/29/2022

Manifold Interpolating Optimal-Transport Flows for Trajectory Inference

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

Guillaume Huguet, et al. ∙

research

∙ 06/15/2022

Taxonomy of Benchmarks in Graph Representation Learning

Graph Neural Networks (GNNs) extend the success of neural networks to gr...

Renming Liu, et al. ∙

research

∙ 06/15/2022

Diffusion Transport Alignment

The integration of multimodal data presents a challenge in cases when th...

Andrés F. Duque, et al. ∙

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...

Yimeng Min, et al. ∙

research

∙ 03/28/2022

Time-inhomogeneous diffusion geometry and topology

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

Guillaume Huguet, et al. ∙

research

∙ 01/22/2022

Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks

Geometric deep learning (GDL) has made great strides towards generalizin...

Frederik Wenkel, et al. ∙

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...

Michal Gerasimiuk, et al. ∙

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...

Guy Wolf, et al. ∙

research

∙ 10/27/2021

Towards a Taxonomy of Graph Learning Datasets

Graph neural networks (GNNs) have attracted much attention due to their ...

Renming Liu, et al. ∙

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...

Alexander Tong, et al. ∙

research

∙ 07/20/2021

Parametric Scattering Networks

The wavelet scattering transform creates geometric invariants and deform...

Shanel Gauthier, et al. ∙

research

∙ 07/15/2021

Hierarchical graph neural nets can capture long-range interactions

Graph neural networks (GNNs) based on message passing between neighborin...

Ladislav Rampasek, et al. ∙

research

∙ 02/25/2021

Diffusion Earth Mover's Distance and Distribution Embeddings

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

Alexander Tong, et al. ∙

research

∙ 02/12/2021

Multimodal data visualization, denoising and clustering with integrated diffusion

We propose a method called integrated diffusion for combining multimodal...

Manik Kuchroo, et al. ∙

research

∙ 01/31/2021

Visualizing High-Dimensional Trajectories on the Loss-Landscape of ANNs

Training artificial neural networks requires the optimization of highly ...

Stefan Horoi, et al. ∙

research

∙ 10/28/2020

Geometric Scattering Attention Networks

Geometric scattering has recently gained recognition in graph representa...

Yimeng Min, et al. ∙

research

∙ 10/06/2020

Data-Driven Learning of Geometric Scattering Networks

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

Alexander Tong, et al. ∙

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...

Andrés F. Duque, et al. ∙

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...

Victor Geadah, et al. ∙

research

∙ 06/15/2020

Supervised Visualization for Data Exploration

Dimensionality reduction is often used as an initial step in data explor...

Jake S. Rhodes, et al. ∙

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...

Bastian Rieck, et al. ∙

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...

Egbert Castro, et al. ∙

research

∙ 03/18/2020

Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks

Graph convolutional networks (GCNs) have shown promising results in proc...

Yimeng Min, et al. ∙

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...

Mostafa Elaraby, et al. ∙

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...

Alexander Tong, et al. ∙

research

∙ 01/09/2020

Internal representation dynamics and geometry in recurrent neural networks

The efficiency of recurrent neural networks (RNNs) in dealing with seque...

Stefan Horoi, et al. ∙

research

∙ 11/14/2019

Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms

The scattering transform is a multilayered wavelet-based deep learning a...

Michael Perlmutter, et al. ∙

research

∙ 07/10/2019

Coarse Graining of Data via Inhomogeneous Diffusion Condensation

Big data often has emergent structure that exists at multiple levels of ...

Nathan Brugnone, et al. ∙

research

∙ 06/25/2019

Visualizing High Dimensional Dynamical Processes

Manifold learning techniques for dynamical systems and time series have ...

Andrés F. Duque, et al. ∙

research

∙ 05/26/2019

A Lipschitz-constrained anomaly discriminator framework

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

Alexander Tong, et al. ∙

research

∙ 05/24/2019

Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds

The Euclidean scattering transform was introduced nearly a decade ago to...

Michael Perlmutter, et al. ∙

research

∙ 01/31/2019

Compressed Diffusion

Diffusion maps are a commonly used kernel-based method for manifold lear...

Scott Gigante, et al. ∙

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 ...

David van Dijk, et al. ∙

research

∙ 12/15/2018

Geometric Scattering on Manifolds

We present a mathematical model for geometric deep learning based upon a...

Michael Perlmutter, et al. ∙

research

∙ 10/07/2018

Graph Classification with Geometric Scattering

One of the most notable contributions of deep learning is the applicatio...

Feng Gao, et al. ∙

research

∙ 09/30/2018

Graph Spectral Regularization for Neural Network Interpretability

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

Alexander Tong, et al. ∙

research

∙ 09/30/2018

Manifold Alignment with Feature Correspondence

We propose a novel framework for combining datasets via alignment of the...

Jay S. Stanley III, et al. ∙

research

∙ 02/14/2018

Geometry-Based Data Generation

Many generative models attempt to replicate the density of their input d...

Ofir Lindenbaum, et al. ∙

research

∙ 02/10/2018

Modeling Dynamics with Deep Transition-Learning Networks

Markov processes, both classical and higher order, are often used to mod...

David van Dijk, et al. ∙

research

∙ 11/19/2015

Diffusion Representations

Diffusion Maps framework is a kernel based method for manifold learning ...

Moshe Salhov, et al. ∙

Guy Wolf

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