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06/20/2023
Deep graph kernel point processes
Point process models are widely used to analyze asynchronous events occu...
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03/29/2023
The G-invariant graph Laplacian
Graph Laplacian based algorithms for data lying on a manifold have been ...
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06/02/2022
Invertible Neural Networks for Graph Prediction
In this work, we address conditional generation using deep invertible ne...
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05/07/2021
Kernel MMD Two-Sample Tests for Manifold Data
We present a study of kernel MMD two-sample test statistics in the manif...
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01/25/2021
Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
This work studies the spectral convergence of graph Laplacian to the Lap...
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09/24/2019
Scale-Equivariant Neural Networks with Decomposed Convolutional Filters
Encoding the input scale information explicitly into the representation ...
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05/18/2018
Butterfly-Net: Optimal Function Representation Based on Convolutional Neural Networks
Deep networks, especially Convolutional Neural Networks (CNNs), have bee...
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06/05/2017
The Geometry of Nodal Sets and Outlier Detection
Let (M,g) be a compact manifold and let -Δϕ_k = λ_k ϕ_k be the sequence ...
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11/09/2016
