
Geometric Scattering Attention Networks
Geometric scattering has recently gained recognition in graph representa...
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DataDriven Learning of Geometric Scattering Networks
Graph neural networks (GNNs) in general, and graph convolutional network...
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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...
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Advantages of biologicallyinspired adaptive neural activation in RNNs during learning
Dynamic adaptation in singleneuron response plays a fundamental role in...
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Supervised Visualization for Data Exploration
Dimensionality reduction is often used as an initial step in data explor...
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Uncovering the Topology of TimeVarying fMRI Data using Cubical Persistence
Functional magnetic resonance imaging (fMRI) is a crucial technology for...
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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...
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Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks
Graph convolutional networks (GCNs) have shown promising results in proc...
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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...
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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...
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Internal representation dynamics and geometry in recurrent neural networks
The efficiency of recurrent neural networks (RNNs) in dealing with seque...
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Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms
The scattering transform is a multilayered waveletbased deep learning a...
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Coarse Graining of Data via Inhomogeneous Diffusion Condensation
Big data often has emergent structure that exists at multiple levels of ...
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Visualizing High Dimensional Dynamical Processes
Manifold learning techniques for dynamical systems and time series have ...
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A Lipschitzconstrained anomaly discriminator framework
Anomaly detection is a problem of great interest in medicine, finance, a...
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Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds
The Euclidean scattering transform was introduced nearly a decade ago to...
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Compressed Diffusion
Diffusion maps are a commonly used kernelbased method for manifold lear...
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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 ...
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Geometric Scattering on Manifolds
We present a mathematical model for geometric deep learning based upon a...
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Graph Classification with Geometric Scattering
One of the most notable contributions of deep learning is the applicatio...
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Graph Spectral Regularization for Neural Network Interpretability
Deep neural networks can learn meaningful representations of data. Howev...
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Manifold Alignment with Feature Correspondence
We propose a novel framework for combining datasets via alignment of the...
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GeometryBased Data Generation
Many generative models attempt to replicate the density of their input d...
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Modeling Dynamics with Deep TransitionLearning Networks
Markov processes, both classical and higher order, are often used to mod...
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Diffusion Representations
Diffusion Maps framework is a kernel based method for manifold learning ...
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Guy Wolf
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