Guy Wolf

Geometric Scattering Attention Networks

Geometric scattering has recently gained recognition in graph representa...
10/28/2020 ∙ by Yimeng Min, et al. ∙ 0 ∙ share

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Data-Driven Learning of Geometric Scattering Networks

Graph neural networks (GNNs) in general, and graph convolutional network...
10/06/2020 ∙ by Alexander Tong, et al. ∙ 14 ∙ share

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Extendable and invertible manifold learning with geometry regularized autoencoders

A fundamental task in data exploration is to extract simplified low dime...
07/14/2020 ∙ by Andrés F. Duque, et al. ∙ 0 ∙ share

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Advantages of biologically-inspired adaptive neural activation in RNNs during learning

Dynamic adaptation in single-neuron response plays a fundamental role in...
06/22/2020 ∙ by Victor Geadah, et al. ∙ 18 ∙ share

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Supervised Visualization for Data Exploration

Dimensionality reduction is often used as an initial step in data explor...
06/15/2020 ∙ by Jake S. Rhodes, et al. ∙ 13 ∙ share

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Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence

Functional magnetic resonance imaging (fMRI) is a crucial technology for...
06/14/2020 ∙ by Bastian Rieck, et al. ∙ 0 ∙ share

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

Biomolecular graph analysis has recently gained much attention in the em...
06/12/2020 ∙ by Egbert Castro, et al. ∙ 0 ∙ share

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Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks

Graph convolutional networks (GCNs) have shown promising results in proc...
03/18/2020 ∙ by Yimeng Min, et al. ∙ 5 ∙ share

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Identifying Critical Neurons in ANN Architectures using Mixed Integer Programming

We introduce a novel approach to optimize the architecture of deep neura...
02/17/2020 ∙ by Mostafa Elaraby, et al. ∙ 103 ∙ share

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TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics

It is increasingly common to encounter data from dynamic processes captu...
02/09/2020 ∙ by Alexander Tong, et al. ∙ 14 ∙ share

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Internal representation dynamics and geometry in recurrent neural networks

The efficiency of recurrent neural networks (RNNs) in dealing with seque...
01/09/2020 ∙ by Stefan Horoi, et al. ∙ 0 ∙ share

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Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms

The scattering transform is a multilayered wavelet-based deep learning a...
11/14/2019 ∙ by Michael Perlmutter, et al. ∙ 0 ∙ share

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Coarse Graining of Data via Inhomogeneous Diffusion Condensation

Big data often has emergent structure that exists at multiple levels of ...
07/10/2019 ∙ by Nathan Brugnone, et al. ∙ 2 ∙ share

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Visualizing High Dimensional Dynamical Processes

Manifold learning techniques for dynamical systems and time series have ...
06/25/2019 ∙ by Andrés F. Duque, et al. ∙ 15 ∙ share

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A Lipschitz-constrained anomaly discriminator framework

Anomaly detection is a problem of great interest in medicine, finance, a...
05/26/2019 ∙ by Alexander Tong, et al. ∙ 0 ∙ share

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Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds

The Euclidean scattering transform was introduced nearly a decade ago to...
05/24/2019 ∙ by Michael Perlmutter, et al. ∙ 0 ∙ share

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Compressed Diffusion

Diffusion maps are a commonly used kernel-based method for manifold lear...
01/31/2019 ∙ by Scott Gigante, et al. ∙ 0 ∙ share

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Finding Archetypal Spaces for Data Using Neural Networks

Archetypal analysis is a type of factor analysis where data is fit by a ...
01/25/2019 ∙ by David van Dijk, et al. ∙ 0 ∙ share

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Geometric Scattering on Manifolds

We present a mathematical model for geometric deep learning based upon a...
12/15/2018 ∙ by Michael Perlmutter, et al. ∙ 0 ∙ share

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Graph Classification with Geometric Scattering

One of the most notable contributions of deep learning is the applicatio...
10/07/2018 ∙ by Feng Gao, et al. ∙ 0 ∙ share

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Graph Spectral Regularization for Neural Network Interpretability

Deep neural networks can learn meaningful representations of data. Howev...
09/30/2018 ∙ by Alexander Tong, et al. ∙ 0 ∙ share

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Manifold Alignment with Feature Correspondence

We propose a novel framework for combining datasets via alignment of the...
09/30/2018 ∙ by Jay S. Stanley III, et al. ∙ 0 ∙ share

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Geometry-Based Data Generation

Many generative models attempt to replicate the density of their input d...
02/14/2018 ∙ by Ofir Lindenbaum, et al. ∙ 0 ∙ share

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Modeling Dynamics with Deep Transition-Learning Networks

Markov processes, both classical and higher order, are often used to mod...
02/10/2018 ∙ by David van Dijk, et al. ∙ 0 ∙ share

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Diffusion Representations

Diffusion Maps framework is a kernel based method for manifold learning ...
11/19/2015 ∙ by Moshe Salhov, et al. ∙ 0 ∙ share

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Guy Wolf

Featured Co-authors

Smita Krishnaswamy

Feng Gao

Kevin R. Moon

Bastian Rieck

Amir Averbuch

Karsten Borgwardt

Matthew Hirn

David van Dijk

Ofir Lindenbaum

Guillaume Lajoie

Margarida Carvalho

Geometric Scattering Attention Networks

Data-Driven Learning of Geometric Scattering Networks

Extendable and invertible manifold learning with geometry regularized autoencoders

Advantages of biologically-inspired adaptive neural activation in RNNs during learning

Supervised Visualization for Data Exploration

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence

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

Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks

Identifying Critical Neurons in ANN Architectures using Mixed Integer Programming

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics

Internal representation dynamics and geometry in recurrent neural networks

Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms

Coarse Graining of Data via Inhomogeneous Diffusion Condensation

Visualizing High Dimensional Dynamical Processes

A Lipschitz-constrained anomaly discriminator framework

Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds

Compressed Diffusion

Finding Archetypal Spaces for Data Using Neural Networks

Geometric Scattering on Manifolds

Graph Classification with Geometric Scattering

Graph Spectral Regularization for Neural Network Interpretability

Manifold Alignment with Feature Correspondence

Geometry-Based Data Generation

Modeling Dynamics with Deep Transition-Learning Networks

Diffusion Representations