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06/15/2023
Hyperbolic Convolution via Kernel Point Aggregation
Learning representations according to the underlying geometry is of vita...
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08/19/2022
Graph Neural Network Based Node Deployment for Throughput Enhancement
The recent rapid growth in mobile data traffic entails a pressing demand...
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06/04/2022
An Unpooling Layer for Graph Generation
We propose a novel and trainable graph unpooling layer for effective gra...
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02/04/2022
Robust Vector Quantized-Variational Autoencoder
Image generative models can learn the distributions of the training data...
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01/30/2022
Hyperbolic Neural Networks for Molecular Generation
With the recent advance of deep learning, neural networks have been exte...
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09/07/2020
Ensemble Riemannian Data Assimilation over the Wasserstein Space
In this paper, we present a new ensemble data assimilation paradigm over...
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06/09/2020
Novelty Detection via Robust Variational Autoencoding
We propose a new method for novelty detection that can tolerate nontrivi...
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03/05/2020
Regularized Variational Data Assimilation for Bias Treatment using the Wasserstein Metric
This paper presents a new variational data assimilation (VDA) approach f...
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03/30/2019
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
We propose a neural network for unsupervised anomaly detection with a no...
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09/28/2018
Graph Generation via Scattering
Generative networks have made it possible to generate meaningful signals...
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08/04/2018
On Lipschitz Bounds of General Convolutional Neural Networks
Many convolutional neural networks (CNNs) have a feed-forward structure....
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03/31/2018
Graph Convolutional Neural Networks via Scattering
We generalize the scattering transform to graphs and consequently constr...
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03/10/2014