b'Guy Wolf'

Diffusion Earth Mover's Distance and Distribution Embeddings

We propose a new fast method of measuring distances between large number...
02/25/2021 ∙ by Alexander Tong, et al. ∙ 0 ∙ share

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Multimodal data visualization, denoising and clustering with integrated diffusion

We propose a method called integrated diffusion for combining multimodal...
02/12/2021 ∙ by Manik Kuchroo, et al. ∙ 0 ∙ share

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

Training artificial neural networks requires the optimization of highly ...
01/31/2021 ∙ by Stefan Horoi, et al. ∙ 0 ∙ share

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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

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