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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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06/12/2023
G-invariant diffusion maps
The diffusion maps embedding of data lying on a manifold have shown succ...
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06/02/2023
Neural Differential Recurrent Neural Network with Adaptive Time Steps
The neural Ordinary Differential Equation (ODE) model has shown success ...
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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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11/21/2022
Spatio-temporal point processes with deep non-stationary kernels
Point process data are becoming ubiquitous in modern applications, such ...
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10/31/2022
Training Neural Networks for Sequential Change-point Detection
Detecting an abrupt distributional shift of the data stream, known as ch...
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09/16/2022
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling
The Gaussian kernel and its traditional normalizations (e.g., row-stocha...
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07/07/2022
Neural Stein critics with staged L^2-regularization
Learning to differentiate model distributions from observed data is a fu...
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06/22/2022
Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise
Bi-stochastic normalization of kernelized graph affinity matrix provides...
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06/14/2022
SpecNet2: Orthogonalization-free spectral embedding by neural networks
Spectral methods which represent data points by eigenvectors of kernel m...
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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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02/17/2022
Training neural networks using monotone variational inequality
Despite the vast empirical success of neural networks, theoretical under...
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02/08/2022
Police Text Analysis: Topic Modeling and Spatial Relative Density Estimation
We analyze a large corpus of police incident narrative documents in unde...
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06/20/2021
Neural Spectral Marked Point Processes
Self- and mutually-exciting point processes are popular models in machin...
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06/06/2021
Neural Tangent Kernel Maximum Mean Discrepancy
We present a novel neural network Maximum Mean Discrepancy (MMD) statist...
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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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02/28/2021
Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples
The Gaussian-smoothed optimal transport (GOT) framework, recently propos...
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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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11/03/2020
Convergence of Graph Laplacian with kNN Self-tuned Kernels
Kernelized Gram matrix W constructed from data points {x_i}_i=1^N as W_i...
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09/04/2020
ACDC: Weight Sharing in Atom-Coefficient Decomposed Convolution
Convolutional Neural Networks (CNNs) are known to be significantly over-...
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08/04/2020
Graph Neural Networks with Low-rank Learnable Local Filters
For the classification of graph data consisting of features sampled on a...
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12/09/2019
Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization
Structured CNN designed using the prior information of problems potentia...
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09/25/2019
Classification Logit Two-sample Testing by Neural Networks
The recent success of generative adversarial networks and variational le...
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09/25/2019
Stochastic Conditional Generative Networks with Basis Decomposition
While generative adversarial networks (GANs) have revolutionized machine...
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09/25/2019
Domain-invariant Learning using Adaptive Filter Decomposition
Domain shifts are frequently encountered in real-world scenarios. In thi...
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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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10/25/2018
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian
The extraction of clusters from a dataset which includes multiple cluste...
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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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05/17/2018
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks
Explicit encoding of group actions in deep features makes it possible fo...
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03/28/2018
Defending against Adversarial Images using Basis Functions Transformations
We study the effectiveness of various approaches that defend against adv...
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02/12/2018
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Filters in a Convolutional Neural Network (CNN) contain model parameters...
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09/14/2017
Two-sample Statistics Based on Anisotropic Kernels
The paper introduces a new kernel-based Maximum Mean Discrepancy (MMD) s...
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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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05/24/2017
Provable Estimation of the Number of Blocks in Block Models
Community detection is a fundamental unsupervised learning problem for u...
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11/09/2016
On the Diffusion Geometry of Graph Laplacians and Applications
We study directed, weighted graphs G=(V,E) and consider the (not necessa...
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02/06/2016
A Deep Learning Approach to Unsupervised Ensemble Learning
We show how deep learning methods can be applied in the context of crowd...
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06/09/2014